Surveymethodology. The purpose of this paper is to review the AES systems literature pertaining to scoring extended-response items in language writing exams. Using Google Scholar, EBSCO and ERIC, we searched the terms "AES", "Automated Essay Scoring", "Automated Essay Grading", or "Automatic Essay" for essays written in English Kamu termasuk orang yang senang dengan karya orang lain? Jika iya, bagaimana kalau sekalian menilai karya-karya tersebut melalui review text? Yuk, kenali review text terlebih dahulu melalui artikel ini! — Yes, hai everyone! Pembahasan mengenai jenis teks dalam bahasa Inggris masih berlanjut, nih. Terakhir, English Academy sudah berhasil mengajakmu untuk mempelajari tentang recount text, spoof text, dan news item text. Sekarang, artikel ini akan menyajikan penjelasan mengenai review text. Jenis teks satu ini cocok bagi kamu yang detail oriented. Pasalnya, review text adalah sebuah tulisan yang berkaitan dengan bagaimana seseorang menilai sesuatu secara rinci. Oke, kita akan langsung ke pembahasan ini ya, mulai dari definisi review text, struktur, tujuan, dan juga contohnya. Keep going on! Apa Itu Review Text Pengertian dari review text adalah jenis teks dalam bahasa Inggris yang berisi ulasan, evaluasi, tinjauan, atau penilaian dari sebuah produk. Dalam hal ini, produk bisa mengacu pada banyak hal, mulai dari publikasi berbentuk buku, film, musik, video, etc. Selain itu, jika kamu juga bisa menuliskan ulasan dalam bentuk review text untuk barang atau jasa, lo. Misal kamu menuliskan ulasan tentang merek mobil, skincare, wedding organizer, dan masih banyak lagi. Kalau mengutip dari British Course, review text jika didefinisikan dalam bahasa Inggris adalah, “Review text is an evaluation of a publication, such as a movie, video game, musical composition, book; a piece of hardware like a car, home appliance, or computer; or an event or performance, such as a live music concert, a play, musical theatre show or dance show.” Nah, dalam review text, biasanya penulis akan menjelaskan bagaimana kekurangan dan kelebihan dari produk yang mereka gunakan. Jadi, memang idealnya penulis harus merasakan atau menggunakan terlebih dahulu produk/karya yang dipakai untuk bisa membuat teks review. Apa Saja Struktur Review Text Generic Structure of Review Text Seperti biasa, ketika ingin membuat teks apa pun itu, pasti ada struktur yang bisa jadi acuan bagi penulis. Review text tersusun dari 1. Introduction As always, pada bagian awal akan selalu ada pengenalan. Kalau dalam narrative text, bagian ini disebut juga dengan orientasi. Orientasi dalam teks review disebut dengan introduction. Dalam introduction atau orientasi, kamu wajib memperkenalkan suatu produk/karya yang akan dibahas pada audiens. Mulai dari apa nama produknya, siapa yang membuatnya, sejarahnya, fungsi dan kegunaannya, atau gambaran umum mengenai sesuatu yang akan di-review. Jangan lupa untuk mengenalkan karya atau benda tersebut dengan jelas agar pembaca tidak misunderstanding ya. Terlebih, dalam penulisan nama karya atau produk jangan sampai ada salah penulisan atau typo, guys. Penasaran sejauh mana kemampuan bahasa Inggris kamu? Yuk, cari tahu melalui Placement Test by English Academy! Ssst, tesnya bersertifikat dan kamu akan berkesempatan untuk berkonsultasi langsung dengan Personal Consultant English Academy, lo! Yuk, cobain sekarang! 2. Evaluation Struktur yang selanjutnya adalah evaluation. Dalam bahasa Indonesia, tentu artinya adalah evaluasi. Pada bagian ini, kamu bisa menggambarkan suatu produk atau karya dengan lebih detail. Well, di paragraf evaluation umumnya berisi tentang kelebihan, keunikan, kualitas, dan apa yang menurutmu mencolok dari sebuah karya atau produk. Contohnya, kalau kamu membuat review text tentang skincare B untuk kulit berjerawat, maka sebutkan apa saja kandungan dari skincare tersebut, bagaimana kemasannya, lalu apakah efektif untuk menyembuhkan jerawat, dan lain sebagainya. Ingat, jangan sampai membuat review text jika kamu tidak mencoba dan menikmati suatu produk/karya terlebih dahulu ya! 3. Interpretative Evaluation akan diikuti dengan interpretation. Sebagian orang menyebut bagian ini dengan interpretative recount, yaitu paragraf yang berisi pandangan penulis terhadap karya atau produk yang diulas. Nah, dalam interpretative juga kamu bisa melakukan komparasi alias menunjukkan perbandingan dengan karya atau produk lain yang sejenis. Tujuan dari perbandingan tersebut adalah untuk memperkuat pandangan penulis terhadap suatu produk. Misal, Caca mengulas serum pencerah wajah merek A dan membandingkannya dengan merek B. Kemudian Caca berpendapat bahwa serum merek A lebih worth to buy karena hasilnya dapat terlihat dalam waktu 1 bulan, sedangkan merek B tidak membuahkan hasil sama sekali. 4. Evaluative Summation / Summary Yap, summary adalah kesimpulan. Jadi, setelah rampung menulis tiga struktur sebelumnya, pada bagian ini penulis dapat memberi kesimpulan sebagai akhir dari review text. Kesimpulan adalah opini terakhir dari penulis. Dalam hal ini, penulis bisa juga menambahkan kritik dan saran untuk owner, creator, atau author. Selain itu, bagian ini berfungsi untuk memberikan penegasan pada audiens apakah suatu produk/karya recommended untuk digunakan atau tidak. Apa Tujuan Dari Review Text? Purpose of Review Text Dalam menulis sebuah teks, pasti ada tujuan dan fungsi yang ingin dicapai, guys. Ini dia tujuan dari review text 1. Memberikan informasi baru berupa gambaran produk, evaluasi, dan kritik untuk pembaca atau khalayak umum. 2. Menjelaskan secara detail terkait kualitas, kelebihan, dan juga kekurangan yang ada pada suatu produk atau karya. Harapannya, audiens yang membaca review text bisa mendapat gambaran yang lebih rinci sebelum akhirnya mereka memutuskan untuk membeli atau menikmati suatu produk/karya. Contoh, kamu ingin membeli sebuah laptop A, tetapi bingung apakah laptop tersebut akan sesuai dengan kebutuhan atau tidak. Nah, kalau ada review text tentang laptop A, pasti kamu akan lebih terbantu, bukan? 3. Selain untuk khalayak umum, jenis teks yang satu ini akan berpengaruh terhadap creator, author, atau owner dari sebuah produk. Jika penulis memberikan review text yang bagus, maka secara tidak langsung mereka telah mempromosikan suatu karya atau produk secara Secara tidak langsung, review text dapat kamu jadikan media untuk pemberian input pada produk/publikasi yang dibuat oleh creator, author, atau owner. Pasalnya, jika ada penulis yang menuangkan kritik pada teks review, biasanya akan dilengkapi juga dengan saran. Hal ini tentu bisa menjadi bekal bagi para creator, author, dan owner agar mereka bisa menghasilkan sebuah produk/karya yang lebih baik lagi. Ciri-ciri Review Text Characteristics of Review Text Kira-kira, apa, sih, yang membedakan review text dengan jenis teks bahasa Inggris lainnya? 1. Berisi mengenai opini yang sifat subjektif tergantung bagaimana sudut pandang penulis personal. 2. Dapat memberikan preferensi kepada pembaca. Misal, Ratna mereview sebuah buku genre romantis dengan judul A. Namun, ternyata Ia memberikan bad review karena pada dasarnya Ratna lebih menyukai buku dengan genre petualangan. Hal ini tentu akan kembali pada pilihan pembaca, jadi review Ratna tidak sepenuhnya dapat diterima oleh orang lain. Language Features of Review Text Unsur Kebahasaan Review Text Bahasa Inggris erat kaitannya dengan grammar seperti tenses dan juga part of speech. Kalau dalam teks, biasa disebut juga sebagai unsur kebahasaan. Language features dalam review text terdiri dari 1. Using simple present tense Kalau nggak skip belajar simple present tense melalui artikel Simple Present Tense Pengertian, Kegunaan, Rumus, dan Contoh Kalimat, sepertinya menulis review text bukan hal yang sulit untukmu. Simple present tense adalah bentuk kalimat dengan kata kerja yang menunjukkan suatu aktivitas di masa sekarang. Karena tidak bersifat terikat oleh waktu, maka review text sangat cocok untuk menggunakan tenses yang satu ini. Contoh kalimatnya begini Aroskin Retinol Serum is a serum clear gel texture that feels thick on the skin. Even though it’s thick, it’s not heavy and tends to absorb quickly. In addition, this serum is fragrance-free so it doesn’t emit any scent. 2. Using adjectives Sudah mengenal salah satu part of speech yang satu ini belum? Kita sempat membahasnya bersama melalui artikel 7 Jenis Kata Sifat Adjective Bahasa Inggris Beserta Contohnya guys. Exactly, adjective adalah kata sifat. Umumnya, adjectives pada review text memiliki tujuan untuk memberikan gambaran tentang keadaan sebuah produk atau karya pada pembaca. Contoh dari adjectives seperti bad, good, etc. Kalau melihat contoh pada poin 1, yang termasuk adjective adalah thick, heavy, and fragrance-free. 3. Focus on specific participants Specific participant adalah sesuatu yang memiliki objek tertentu, ia tidak bersifat umum dan hanya ada satu. Nah, karena review text mengulas tentang suatu produk atau karya, tentunya objek yang dituju pun bersifat spesifik. Contohnya kamu ingin mengulas film Doctor Strange in the Multiverse of Madness, judul film tersebut tentu hanya ada satu, kan? Contoh lain adalah novel Laskar Pelangi karya Andrea Hirata, and many more. 4. Using long and complex clauses Actually, penjelasan apa itu long and complex clauses cukup panjang. Tapi intinya, complex clauses adalah kalimat yang terdiri atas satu independent clause serta satu atau lebih dependent clause/subordinate clause. Independent clause adalah inti kalimat yang bisa berdiri sendiri. Sementara itu, dependent clause adalah klausa yang bergantung pada kalimat inti. Nah, biasanya kedua klausa ini dipisahkan dengan tanda koma , untuk membentuk sebuah complex clauses. Selain itu, ditandai juga dengan conjunction atau kata penghubung. Kita coba ambil kalimat dari poin nomor satu Even though the texture of aroskin retinol serum is thick, it’s not heavy and tends to absorb quickly. Pada contoh di atas, terdapat konjungsi even though yang artinya “meski” dengan fungsi menunjukkan pertentangan. Umumnya, tekstur cairan yang kental akan sulit untuk meresap pada kulit, akan tetapi serum dari Aroskin mampu menyerap dengan cukup cepat. 5. Using metaphor or idiom Masih ingat dengan majas? Yap, metaphor atau metafora adalah salah satu jenis majas. Mengutip dari KBBI, metafora adalah kata atau kelompok kata dengan arti yang bukan sebenarnya, melainkan sebagai lukisan yang berdasarkan persamaan atau perbandingan. Misalnya seperti ini Aroskin claims that their retinol serum product can brighten the face within 1 month. I think it’s wrong, because the serum can make my face compete with shiny glass in just 2 weeks. Tujuan dari metaphor tersebut adalah untuk membuat teks lebih enak dibaca, sekaligus bisa mempengaruhi audiens bahwa serum yang diulas memang layak untuk dicoba. Sementara itu, idiom adalah serangkaian kata yang tidak bisa diartikan secara harfiah. Kamu bisa cek penjelasan lengkapnya di artikel 101 Idioms yang Tidak Bisa Diterjemahkan Secara Harfiah ya! Contohnya adalah sebagai berikut I think this music can make a lot of people feel as snug as a bug in a rug. Kalau secara harfiah, snug as a bug in a rug artinya adalah nyaman seperti serangga di permadani. Padahal, makna yang sebenarnya adalah “senang/nyaman” Apa Perbedaan Review Text dan Resensi? Mungkin kamu sedikit bingung, apa perbedaan review text dan juga resensi. Jika menyimpulkan dari berbagai sumber, review text adalah teks dalam bahasa Inggris yang berfungsi untuk mengulas mulai dari barang, jasa, atau karya. Berbeda dengan resensi yang identik dengan ulasan terhadap suatu karya saja seperti film, buku, atau lagu. Selain itu, dalam resensi biasanya hanya berfokus pada satu produk, alias tidak membandingkan dengan produk lain. Contoh Review Text Example of Review Text Review of Top Gun Maverick Film Introduction Top Gun Maverick is a 2022 American action drama film directed by Joseph Kosinski and written by Ehren Kruger, Eric Warren Singer and Christopher McQuarrie, from a story by Peter Craig and Justin Marks. The sequel to Top Gun 1986, the film stars Tom Cruise as Captain Pete “Maverick” Mitchell reprising his role from the original, alongside Miles Teller, Jennifer Connelly, Jon Hamm, Glen Powell, Lewis Pullman, Ed Harris, and Val Kilmer. Top Gun Maverick released in the box office on May 27, 2022 by Paramount Pictures. Evaluation This film tells the story of the great naval aviator of more than 30 years, Pete Mitchell who is assigned to lead his juniors on a mission. Pete only has less than three weeks to make all his juniors have the skills and strong mental to carry out the mission. The mission that must be carried out is quite dangerous, so the juniors must have jet maneuvering skills and also high concentration. The action of chasing fighter jets in the air is one of the stunning displays and the main attraction of this film. The most unique fact of this film is about Tom Cruise operating a real fighter jet. At first, he was skeptical about this project, but the production team twisted Tom Cruise’s arm by sending him to the Naval Air Facility in El Centro California to ride an F-14 until finally he was convinced to take this film. Interpretative Reportedly, Top Gun Maverick managed to earn Rp 11 trillion and became Tom Cruise’s most successful film. I think they really deserve it. This film not only emphasizes the skill side as a pilot, but there are stories of family, love, friendship, competition, and friendship. This film deserves to be the best film in 2022. Summary If you are a person who likes action or adventure genre films, then this film will amaze you. Tom Cruise’s expertise in “playing” fighter jets amazed the audience. You will have a great experience if you watch it in a 4D cinema. Penjelasan Dari contoh di atas, berikut analisa terkait kaidah kebahasaan yang digunakan ditunjukkan dengan bold 1. Simple present tense Top Gun Maverick is a 2022 American action drama film This film tells the story of the great naval aviator of more than 30 years 2. Using adjectives Have the skills and strong mental to carry out the mission The mission that must be carried out is quite dangerous 3. Using specific participant Top Gun Maverick is a 2022 American action drama film Top Gun Maverick released in the box office on May 27, 2022 by Paramount Pictures Naval Air Facility in El Centro California 4. Using long and complex causes At first, he was skeptic about this project, but the production team twisted Tom Cruise’s arm by sending him to the Naval Air Facility in El Centro California 5. Using metaphor and idioms The production team twisted Tom Cruise’s arm by sending him to the Naval Air Facility in El Centro California Finally, itulah penjelasan mengenai review text yang cukup lengkap. Jadi, kalau kamu ingin menulis ulasan tentang produk atau karya tapi nggak tahu bagaimana caranya, ikuti saja langkah-langkah di artikel ini ya! Ingin lebih mahir writing dalam bahasa Inggris? Yuk, belajar di English Academy bareng pengajar lokal dan internasional! Ketahui dulu bagaimana proses kelasnya melalui Free Trial Class dengan cara klik gambar di bawah ini ya! Textfeatures are the different parts of a nonfiction or fiction text other than the main story itself. They help the reader understand the story. Examples of nonfiction text features include captions, index, and glossary. Examples for fiction include pictures, title, and chapter headings. Keep reading to get a full description of both fiction In linguistics, the term text refers to The original words of something written, printed, or spoken, in contrast to a summary or paraphrase. A coherent stretch of language that may be regarded as an object of critical analysis. Text linguistics refers to a form of discourse analysis—a method of studying written or spoken language—that is concerned with the description and analysis of extended texts those beyond the level of the single sentence. A text can be any example of written or spoken language, from something as complex as a book or legal document to something as simple as the body of an email or the words on the back of a cereal box. In the humanities, different fields of study concern themselves with different forms of texts. Literary theorists, for example, focus primarily on literary texts—novels, essays, stories, and poems. Legal scholars focus on legal texts such as laws, contracts, decrees, and regulations. Cultural theorists work with a wide variety of texts, including those that may not typically be the subject of studies, such as advertisements, signage, instruction manuals, and other ephemera. Text Definition Traditionally, a text is understood to be a piece of written or spoken material in its primary form as opposed to a paraphrase or summary. A text is any stretch of language that can be understood in context. It may be as simple as 1-2 words such as a stop sign or as complex as a novel. Any sequence of sentences that belong together can be considered a text. Text refers to content rather than form; for example, if you were talking about the text of "Don Quixote," you would be referring to the words in the book, not the physical book itself. Information related to a text, and often printed alongside it—such as an author's name, the publisher, the date of publication, etc.—is known as paratext. The idea of what constitutes a text has evolved over time. In recent years, the dynamics of technology—especially social media—have expanded the notion of the text to include symbols such as emoticons and emojis. A sociologist studying teenage communication, for example, might refer to texts that combine traditional language and graphic symbols. Texts and New Technologies The concept of the text is not a stable one. It is always changing as the technologies for publishing and disseminating texts evolve. In the past, texts were usually presented as printed matter in bound volumes such as pamphlets or books. Today, however, people are more likely to encounter texts in digital space, where the materials are becoming "more fluid," according to linguists David Barton and Carmen Lee " Texts can no longer be thought of as relatively fixed and stable. They are more fluid with the changing affordances of new media. In addition, they are becoming increasingly multimodal and interactive. Links between texts are complex online, and intertextuality is common in online texts as people draw upon and play with other texts available on the web." An example of such intertextuality can be found in any popular news story. An article in The New York Times, for example, may contain embedded tweets from Twitter, links to outside articles, or links to primary sources such as press releases or other documents. With a text such as this, it is sometimes difficult to describe what exactly is part of the text and what is not. An embedded tweet, for instance, may be essential to understanding the text around it—and therefore part of the text itself—but it is also its own independent text. On social media sites such as Facebook and Twitter, as well as blogs and Wikipedia, it is common to find such relationships between texts. Text linguistics is a field of study where texts are treated as communication systems. The analysis deals with stretches of language beyond the single sentence and focuses particularly on context, information that goes along with what is said and written. Context includes such things as the social relationship between two speakers or correspondents, the place where communication occurs, and non-verbal information such as body language. Linguists use this contextual information to describe the "socio-cultural environment" in which a text exists. Sources Barton, David, and Carmen Lee. "Language Online Investigating Digital Texts and Practices." Routledge, Ronald, and Michael McCarthy. "Cambridge Grammar of English." Cambridge University Press, Marvin K. L., et al. "Linguistic Perspectives on Literature." Routledge, 2015.

Thebasic utility of any language feature is to help the reader to better understand the texts. A writer uses numerous language techniques and literary device to make their writing more impressive and have the best impact on the reader. Language features help you to distinguish between these techniques and simplify the text language so that it

AbstractOnline reviews play a critical role in customer’s purchase decision making process on the web. The reviews are often ranked based on user helpfulness votes to minimize the review information overload problem. This paper examines the factors that contribute towards helpfulness of online reviews and builds a predictive model. The proposed predictive model extracts novel linguistic category features by analysing the textual content of reviews. In addition, the model makes use of review metadata, subjectivity and readability related features for helpfulness prediction. Our experimental analysis on two real-life review datasets reveals that a hybrid set of features deliver the best predictive accuracy. We also show that the proposed linguistic category features are better predictors of review helpfulness for experience goods such as books, music, and video games. The findings of this study can provide new insights to e-commerce retailers for better organization and ranking of online reviews and help customers in making better product advent of Web has enabled users to share their opinions, experiences and knowledge via blogs, forums, and other social media websites. In the e-commerce context, Web allows consumers to share their purchase and usage experiences in the form of product reviews Amazon product reviews, CNET reviews. Such reviews contain valuable information and are often used by potential customers for making purchase decisions. However, some of the most popular products receive several hundreds or thousands of reviews resulting in a review information overload problem. Besides, the review quality across large volume of reviews exhibits wide variations Liu et al., 2008, Tsur and Rappoport, 2009.In order to help potential customers in navigating through large volume of reviews, e-commerce websites provide an interactive voting feature. For example, Amazon asks its review viewers “Was this review helpful? Yes/No” to get user votes on reviews. The votes thus gathered from multiple users are then aggregated, ranked and presented, “24 of 36 people found the following review helpful”. Reviews with higher share of helpful votes are ranked higher than the ones with lower helpful votes. This paper aims to study the factors that play an important role for a review to get higher helpful votes. Such an analysis is important for the following reasons First, reviews can be effectively summarized by filtering low quality reviews. Second, websites that do not use voting feature could benefit from an automated helpfulness prediction system. Third, review ranking system could be improved with a better understanding of the underlying review helpfulness factors, avoiding early bird bias problem Liu, Cao, Lin, Huang, & Zhou, 2007.The review voting behaviour which influences review helpfulness can be visualized as a socio-psychological process between the reviewer and the reviewee. This process is facilitated by Web as a communication medium. Language plays a very important role in this process between the reviewer and reviewee. In an offline world, communication between a sender and receiver is often influenced by non-verbal cues, communication contexts and past interactions between the sender and receiver. In the absence of such external factors in the online world, language plays a crucial role. The sender’s message composed using a language impacts the receivers cognition and influences their behaviour. As the sender’s message can be composed in numerous ways, its impact on the receivers cognition and behaviour varies. Our basic intuition is that the review voting behaviour can be better understood by studying the psychological properties and propensities of the language. The Linguistic Category Model LCM proposed by Semin and Fiedler 1991 is a conceptual framework that models psychological properties of the language. The linguistic categories used in the LCM model and their descriptions are presented in Table LCM model Coenen et al., 2006, Semin and Fiedler, 1991 uses three broad linguistic categories, namely Adjectives fantastic, excellent, beautiful, State verbs love, hate, envy and Action verbs. The action verbs are further sub-divided into State Action Verbs amaze, anger, shock, Interpretive Action Verbs help, avoid, recommend, and Descriptive Action Verbs call, talk, run. All of these linguistic categories are organized on a abstract-to-concrete dimension. At one extreme ADJ the terms are abstract, less verifiable, more disputable and least informative. While at the other extreme DAVs, the terms are concrete, verifiable, less disputable and most the following three review examples tagged with key linguistic categories fantastic ADJ camera. The picture quality of this camera is wonderful ADJ. is my first camera and I love SV it. The camera is excellent ADJ. regularly takeDAV pics with this camera. The quality of the pics has really amazed SAV me. Battery life is fabulous ADJ. My only issue is that it makes DAV a lot of noise in autofocus mode. I strongly recommend IAV this 1 is highly abstract and subjective as it primarily uses adjectives. Review 2 uses a subjective verb love’ indicating the emotional state of the reviewer. The last review provides a more concrete and objective description of the camera using DAVs. Besides, it also contains subjective ADJ opinion of the reviewer. It is evident that the review 3 with far more concrete and descriptive information is likely to be more helpful than other two reviews for purchase decision making. Therefore, our basic intuition is that the linguistic categories impact the receivers or consumers cognitive process, influence their voting behaviour and affect review this paper, our objective is to examine the use of such linguistic category features for predicting review helpfulness. We make a first attempt at devising a new method for extracting linguistic category features from review text and build a binary classification model. We conduct a detailed experimental analysis on two real-life review datasets to demonstrate the utility of the proposed linguistic features. Furthermore, we study the effect of product type on review helpfulness and show that the proposed linguistic features are better predictors of review helpfulness for experience rest of the paper is organized as follows. Section 2 describes the related work on review helpfulness. Section 3 elucidates the proposed novel features used in the model. Subsequently, Section 4 presents detailed experimental analysis, results and discussions. Section 5 highlights the implications of this research to theory and practice. Finally, Section 6 provides concluding remarks and outlines directions for future research snippetsRelated literatureZhang and Varadarajan 2006 build a regression model for predicting the utility of product reviews. They use lexical similarity, syntactic terms based on Part-Of-Speech POS, and lexical subjectivity as features. Mudambi and Schuff 2010 formulated a linear regression model for determining factors that contribute towards review helpfulness. Their work was replicated by Huang and Yen 2013 and achieved just 15% explanatory power. The authors conclude that the review helpfulness predictionReview helpfulness modelWe first describe the terminology used in this paper and formally define the problem. Then, we explain the features used in our prediction review datasetsWe used two real-life datasets for the experimentation. First dataset is a publicly available multi-domain sentiment analysis dataset Blitzer, Dredze, & Pereira, 2007. This dataset has 13120 customer reviews across four different product categories. The second dataset, a more recent review dataset, is obtained by crawling website. The details of both the datasets are summarized in Table datasets are cleaned and prepared for analysis by applying the following threeImplicationsThe findings of this paper has implications for both theory and practice. From a theoretical perspective, the paper brings fresh ideas into the expert and intelligent systems research community from social psychology literature. The basic ideas for the linguistic category features introduced in this paper are borrowed from the LCM model Semin & Fiedler, 1991 used in psychology literature. Another important contribution of this paper is the design of automatic linguistic category featureConclusionsThis paper examined the online review helpfulness problem and built a new prediction model. The proposed model used hybrid set of features review metadata, subjectivity, readability, and linguistic category to predict review helpfulness. The effectiveness of the proposed model was empirically evaluated on two real-life review datasets. The linguistic category features was found to be effective in predicting helpfulness of experience paper described an automatic linguistic categoryReferences 30 et determinants of voting for the helpfulness of online user reviews A text mining approachDecision Support Systems2011N. Korfiatis et content quality and helpfulness of online product reviews The interplay of review helpfulness vs. review contentElectronic Commerce Research and Applications2012S. Lee et the helpfulness of online reviews using multilayer perceptron neural networksExpert Systems with Applications2014Z. Liu et makes a useful online review? Implication for travel product websitesTourism Management2015 Ngo-Ye et influence of reviewer engagement characteristics on online review helpfulness A text regression modelDecision Support Systems2014Y. Pan et unequal A study of the helpfulness of user-generated product reviewsJournal of Retailing2011S. Baccianella et An enhanced lexical resource for sentiment analysis and opinion miningBird, S. 2006. Nltk The natural language toolkit. In Proceedings of the COLING/ACL on interactive presentation...Blitzer, J., Dredze, M., & Pereira, F. 2007. Biographies, bollywood, boomboxes and blenders Domain adaptation for...L. BreimanRandom forestsMachine Learning2001 Chang et A library for support vector machinesThe ACM Transactions on Interactive Intelligent Systems2011 Chua et review helpfulness as a function of reviewer reputation, review rating, and review depthJournal of the Association for Information Science and Technology2014Coenen, L. H. M., Hedebouw, L., & Semin, G. R. 2006. The Linguistic Category Model LCM. Retrieved from...DuBay, W. H. 2004. The principles of readability. Impact Information....A. Ghose et the helpfulness and economic impact of product reviews Mining text and reviewer characteristicsIEEE Transactions on Knowledge and Data Engineering2011Cited by 151Complementary or Substitutive? A Novel Deep Learning Method to Leverage Text-image Interactions for Multimodal Review Helpfulness Prediction2022, Expert Systems with ApplicationsSpecifically, the review-related features are exemplified by review sentiment extremity Li, Wu, & Mai, 2019, review timeliness Liu et al., 2008, review length Hong et al., 2017, writing style Siering, Muntermann, & Rajagopalan, 2018, etc. The textual semantic features of reviews such as multilingual characteristics Zhang & Lin, 2018, linguistic features Krishnamoorthy, 2015 were also verified as being of great importance to the RHP. To better leverage textual review information, researchers also adopted deep learning models to obtain powerful hidden representation features of the review texts Kong et al., 2020; Chen et al., 2018.View all citing articles on ScopusRecommended articles 6View full textCopyright © 2015 Elsevier Ltd. All rights reserved. Inthis part, the book takes a glance at cur rent issues being studied and here the author reminds. us at the same time that Linguistics, like lan guage itself, is dynamic and th erefore subject
Due to the development of e-commerce and web technology, most of online Merchant sites are able to write comments about purchasing products for customer. Customer reviews expressed opinion about products or services which are collectively referred to as customer feedback data. Opinion extraction about products from customer reviews is becoming an interesting area of research and it is motivated to develop an automatic opinion mining application for users. Therefore, efficient method and techniques are needed to extract opinions from reviews. In this paper, we proposed a novel idea to find opinion words or phrases for each feature from customer reviews in an efficient way. Our focus in this paper is to get the patterns of opinion words/phrases about the feature of product from the review text through adjective, adverb, verb, and noun. The extracted features and opinions are useful for generating a meaningful summary that can provide significant informative resource to help the user as well as merchants to track the most suitable choice of IntroductionMuch of the existing research on textual information processing has been focused on mining and retrieval of factual information. Little works had been done on the process of mining opinions until only recently. Automatic extraction of customers’ opinions can better benefit both customers and manufacturers. Product review mining can provide effective information that are classifying customer reviews as “recommended” or “not recommended” based on customers’ opinions for each product feature. In this cases, customer reviews highlight opinion about product features from various Merchant sites. However, many reviews are so long and only a few sentences contain opinions for product a popular product, the number of reviews can be in hundreds or even in thousands, which is difficult to be read one by one. Therefore, automatic extraction and summarization of opinion are required for each feature. Actually, when a user expresses opinion for a product, he/she states about the product as a whole or about its features one by one. Feature identification in product is the first step of opinion mining application and opinion words extraction is the second step which is critical to generate a useful summary by classifying polarity of opinion for each feature. Therefore, we have to extract opinion for each feature of a this paper, we take a written review as input and produce a summary review as output. Given a set of customer reviews of a particular product, we need to perform the following tasks1identifying product feature that customer commented on;2extracting opinion words or phrases through adjective, adverb, verb, and noun and determining the orientation;3generating the use a part-of-speech tagger to identify phrases in the input text that contains adjective or adverb or verb or nouns as opinion phrases. A phrase has a positive semantic orientation when it has good associations “awesome camera” and a negative semantic orientation when it has bad associations “low battery”.The rest of the paper is organized as follows. Section 2 describes the related work of this paper. Section 3 elaborates theoretical background for opinion mining. Section 4 expresses methodology and experiments of the system and Section 5 describes are several techniques to perform opinion mining tasks. In this section, we discuss others’ related works for feature extraction and opinion words extraction. Hu and Liu [1] proposed several methods to analyze customer reviews of format 3. They perform the same tasks of identifying product features on which customers have expressed their opinions and determining whether the opinions are positive or negative. However, their techniques, which are primarily based on unsupervised item sets mining or association rule mining, are only suitable for reviews of formats 3 and 1 to extract product features. Then, frequent item sets of nouns in reviews are likely to be product features while the infrequent ones are less likely to be product features. This work also introduced the idea of using opinion words to find additional often infrequent of these formats usually consist of full sentences. The techniques are not suitable for pros and cons of format 2, which are very brief. Liu et al. [2] presented how to extract product features from “Pros” and “Cons” as type of review format 2. They proposed a supervised pattern mining method to find language patterns to identify product features. They do not need to determine opinion orientations because of using review format 2 indicated by “Pros” and “Cons.”Hu and Liu [3] proposed a number of techniques based on data mining and natural language processing methods to mine opinion/product features. It is mainly related to text summarization and terminology identification. Their system does not mine product features and their work does not need a training corpus to build a summary. Su et al. [4] proposed a novel mutual reinforcement approach to deal with the feature-level opinion mining problem. Their approach predicted opinions relating to different product features without the explicit appearance of product feature words in reviews. They aim to mine the hidden sentiment link between product features and opinion words and then build the association approach for mining product feature and opinion based on consideration of syntactic information and semantic information in [5]. The methods acquire relations based on fixed position of words. However, the approaches are not effective for many cases. Turney [6] presented a simple unsupervised learning algorithm for classifying reviews as recommended thumbs up or not recommended thumbs down. The classification of a review is predicted by the average semantic orientation of the phrases in the review that contains adjectives or adverbs. Wu et al. [7] implemented extracting relations between product feature and expressions of opinions. The relation extraction is an important subtask of opinion mining for the relations between more than one product features and different opinion words on each of and Lam [8, 9] employ hidden Markov models and conditional random fields, respectively, as the underlying learning method for extracting product features. Pang et al. [10], Mras and Carroll [11], and Gamon [12] use the data of movie review, customer feedback review, and product review. They use the several statistical feature selection methods and directly apply the machine learning techniques. These experiments show that machine learning techniques only are not well performing on sentiment classification. They show that the presence or absence of word seems to be more indicative of the content rather than the frequency for a word. Zhang and Liu [13] aimed to identify such opinionated noun features. Their involved sentences are also objective sentences but imply positive or negative opinions. They proposed a method to deal with the problem for finding product features which are nouns or noun phrases that are not subjective but Mining Opinion for Feature LevelIn this paper, we only focus on mining opinions for feature level. This task is not only technically challenging because of the need for natural language processing, but also very useful in practice. For example, businesses always want to find public or consumer opinions about their products and services from the commercial web sites. Potential customers also want to know the opinions of existing users before they use a service or purchase a product. Moreover, opinion mining can also provide valuable information for placing advertisements in commercial web pages. If in a page people express positive opinions or sentiments on a product, it may be a good idea to place an ad of the product. However, if people express negative opinions about the product, it is probably not wise to place an ad of the product. A better idea may be to place an ad of a competitor’s are three main review formats on the Web. Different review formats may need different techniques to perform the opinion extraction 1—pros and cons The reviewer is asked to describe pros and cons 2—pros, cons, and detailed review the reviewer is asked to describe pros and cons separately and also write a detailed 3—free format the reviewer can write freely, that is, no separation of pros and the review formats 1 and 2, opinion or semantic orientations positive or negative of the features are known because pros and cons are separated. Only product features need to be identified. We concentrate on review format 3 and we need to identify and extract both product features and opinions. This task goes to the sentence level to discover details, that is, what aspects of an object that people liked or disliked. The object could be a product, a service, a topic, an individual, an organization, and so forth. For instance, in a product review sentence, it identifies product features that have been commented on by the reviewer and determines whether the comments are positive or negative. For example, in the sentence, “The battery life of this camera is too short,” the comment is on “battery life” of the camera object and the opinion is real-life applications require this level of detailed analysis because, in order to make product improvements, one needs to know what components and/or features of the product are liked and disliked by consumers. Such information is not discovered by sentiment and subjectivity classification [14]. To obtain such detailed aspects, we need to go to the sentence level. Two tasks are apparent.1Identifying and extracting features of the product that the reviewers have expressed their opinions on, called product features for instance, in the sentence “the picture quality of this camera is amazing,” the product feature is “picture quality.”2Determining whether the opinions on the features are positive, negative or neutral. In the above sentence, the opinion on the feature “picture quality” is the sentence, “the battery life of this camera is too short,” the comment is on the “battery life” and the opinion is negative. A structured summary will also be produced from the mining Methodology to Find Patterns for Features and Opinions ExtractionThe goal of OM is to extract customer feedback data such as opinions on products and present information in the most effective way that serves the chosen objectives. Customers express their opinion in review sentences with single words or phrases. We need to extract these opinion words or phrases in efficient way. Pattern extraction approach is useful for commercial web pages in which customers can be able to write comments about products or services. Let us use an example of the following review sentence “The battery life is long.”In this sentence, the feature is “battery life” and opinion word is “long.” Therefore, we first need to identify the feature and opinion from the 1 shows the overall process for generating the results of feature-based opinion summarization. The system input is customer reviews’ datasets. We first need to perform POS tagging to parse the sentence and then identify product features and opinion words. The extracted opinion words/phrases are used to determine the opinion orientation which is positive or negative. Finally, we summarize the opinion for each product feature based on their this paper, we focus on feature extraction and opinion word extraction to provide opinion summarization. In feature extraction phase, we need to perform part-of-speech tagging to identify nouns/noun phrases from the reviews that can be product features. Nouns and noun phrases are most likely to be product tagging is important as it allow us to generate general language patterns. We use Stanford-POS tagger to parse each sentence and yield the part-of-speech tag of each word whether the word is a noun, adjective, verb, adverb, etc. and identify simple noun and verb groups syntactic chunking, for instance,The_DT photo_JJ quality_NN is_VBZ amazing_JJ and_CC i_FW know_VBP i_FW m_VBP going_VBG to_TO have_VB fun_NN with_IN all_PDT the_DT POS tagging is done, we need to extract features that are nouns or noun phrases using the pattern knowledge see Table 1. And then, we focus on identifying domain product features that are talked about by customers by using the manually tagged training corpus for domain opinion words extraction, we used extracted features that are used to find the nearest opinion words with adjective/adverb. To decide the opinion orientation of each sentence, we need to perform three subtasks. First, a set of opinion words adjectives, as they are normally used to express opinions is identified. If an adjective appears near a product feature in a sentence, then it is regarded as an opinion word. We can extract opinion words from the review using the extracted features, for instance;The strap is horrible and gets in the way of parts the camera you need access nearly 800 pictures I have found that this camera takes incredible comes with a rechargeable battery that does not seem to last all that long, especially if you use the flash a the first sentence, the feature, strap, is near the opinion word horrible. And in the second example, feature “picture” is close to the opinion word incredible. We found that opinion words/phrases are mainly adjective/adverb that is used to qualify product features with nouns/noun phrases. In this case, we can extract the nearby adjective as opinion word if the sentences contain any features. However, for the third sentence, the feature, battery, cannot be able to extract nearby adjective to meet the opinion word “long.” The nearby adjective “rechargeable” dose not bear opinion for the feature “battery.”Moreover, both adjective and adverb are good indicators of subjectivity and opinions. Therefore, we need to extract phrases containing adjective, adverb, verb, and noun that imply opinion. We also consider some verbs like, recommend, prefer, appreciate, dislike, and love as opinion words. Some adverbs like not, always, really, never, overall, absolutely, highly, and well are also considered. Therefore, we extract two or three consecutive words from the POS-tagged review if their tag conforms to any of the patterns. We collect all opinionated phrases of mostly 2/3 words like adjective, noun, adjective, noun, noun, adverb, adjective, adverb, adjective, noun, verb, noun, and so forth from the processed POS-tagged resulting patterns are used to match and identify opinion phrases for new reviews after the POS tagging. However, there are more likely opinion words/phrases in the sentence but they are not extracted by any patterns. From these extracted patterns, most of adjectives or adverbs imply opinion for the nearest nouns/noun phrases. Table 2 described some examples of opinion Dataset of the SystemWe used annotated customer reviews’ data set of 5 products for testing. All the reviews are from commercial web sites such as and Each review consists of review title and detail of review text. The reviews are retagged manually based on our own feature list. Each camera review sentence is attached with the mentioned features and their associated opinion words. Therefore, we only focus on the review sentences that contain opinions for product features, for instance, “The pictures are absolutely amazing—the camera captures the minutest of details.” This sentence will receive the tag picture [+3]. Words in the brackets are those we found to be associated with the corresponding opinion orientation of feature whether positive or negative see Table 3. ExperimentsWe carried out the experiments using customer reviews of 5 electronic products two digital cameras, one DVD player, one MP3 player, and one cellular phone. All the reviews are extracted from All of them are used as the training data to mine patterns. These patterns are then used to extract product features from test reviews of these products. We now evaluate the proposed automatic technique to see how effective it is in identifying product features and opinions from customer reviews. In this paper, we only verify only product features but we make sentiment orientation of opinion on that features as an ongoing process. The effectiveness of the proposed system has been verified with review set on these five different electronic products. All the results generated by our system are compared with the manually tagged results. We also assess the time saved by semiautomatic tagging over manual tagging. We showed the comparison results with Hu and Liu’s approach and our approach is slightly higher than their results in Table ConclusionMost of opinion mining researches use a number of techniques for mining opinion and summarizing opinions based on features in product reviews based on data mining and natural language processing methods. Review text is unstructured and only a portion or some sentences include opinion-oriented words. In product reviews, users write comments about features of products to describe their views according to their experience and observations. The first step of opinion mining in classifying reviews’ documents is extracting features and opinion words. Therefore opinion mining system needs only the required sentences to be processed to get knowledge efficiently and effectively. We proposed the ideas to extract patterns of features and/or opinion phrases. We showed results of experiments with extracting pattern knowledge based on linguistic rule. We expected to achieve good results by extracting features and opinion-oriented words from review text with help of adjectives, adverbs, nouns, and verbs. We believe that there is rich potential for future research. For identifying feature, we need to extend both explicit and implicit feature as our future work because both of these features are useful for providing more accurate results in determining the polarity of product/feature before summarizing them, rather than explicit feature Hu and B. 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Languagefeatures - review. STUDY. Flashcards. Learn. Write. Spell. Test. PLAY. Match. Gravity. Created by. mariottiamanda. Terms in this set (29) Descriptive language. Incorporates the authors use of adjectives and adverbs. The way in which a verb or noun is described can be altered to have a desired effect on a reader. We can look at the

TextAnalytics is the process of converting unstructured text data into meaningful data for analysis, to measure customer opinions, product reviews, feedback, to provide search facility, sentimental analysis and entity modeling to support fact based decision making. Text analysis uses many linguistic, statistical, and machine learning techniques.

LanguageFeatures. The e language uses an aspect-oriented programming (AOP) approach, which is an extension of the object-oriented programming approach to specifically address the needs required in functional verification. AOP is a key feature in allowing for users to easily bolt on additional functionality to existing code in a non-invasive
languagefeatures of an argumentative text language features of an argumentative text. coronary vasodilator drugs By On May 11, 2022. 0 Usetext features to locate information (e.g., charts, tables of contents, maps, illustrations). 0301.6.6. Links verified on 12/24/2014. Captions Help Tell the Story - look at three pictures and try to determine which caption fits best ; Chapter Headings - lesson online with exercises for practice ; Chapter Headings Quiz - online quiz ; Parts of a Book - online quiz .
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    • language features of review text