Cover not available
Part of
Corpora in Translation and Contrastive Research in the Digital Age: Recent advances and explorations
Edited by Julia Lavid-López, Carmen Maíz-Arévalo and Juan Rafael Zamorano-Mansilla
[Benjamins Translation Library 158] 2021
► pp. 283–306

Chapter 11
Generic analysis of mobile application reviews in English and Spanish

A contrastive corpus-based study

ORCID logo
In this paper, the Systemic Functional Linguistics (SFL) approach to genre is applied to English and Spanish mobile application reviews from Google Play Store. These reviews are shown to be divisible into two optional stages, namely Evaluation and Description, the first part being more subjective and based on the author’s opinion and the second one more objective and focused on describing issues. The frequency of use of the Attitude axis from Appraisal Theory provides support for the distinction of those stages. The combination of the polarity of the contents of each stage together with the number of stars given to the item reviewed creates six patterns for the reviews. However, no noticeable differences between English and Spanish reviews were observed.
Keywords: product reviews, application stores, appraisal theory, genre, SFL
Article outline
  • Introduction
  • Theoretical framework
    • Genre analysis
    • Appraisal theory
  • The mobile application review genre and the corpus
    • General characteristics of mobile application and game reviews
    • Comparison with reviews about other products and from other platforms
    • Corpus
  • Generic stages in mobile application reviews
    • Lexicogrammatical features of stages
    • Patterns observed in the stages
      • Distribution of staging patterns in the English and Spanish corpora
    • Most important problems in the (automatic) analysis of application and game reviews
  • Summary and concluding remarks
  • References
Published online: 8 December 2021
DOI logo
https://doi.org/10.1075/btl.158.11mor
References (46)
References
Argamon, Shlomo, Kenneth Bloom, Andrea Esuli, and Fabrizio Sebastiani. 2007. “Automatically Determining Attitude Type and Force for Sentiment Analysis.” In Proceedings of the 3rd Language & Technology Conference (LTC-07), 218–231.Google Scholar
Bakhtin, Mikhail Mikhaĭlovich. 1986. Speech genres and other late essays, Vern W. McGee (trans.). Austin: University of Texas Press.Google Scholar
Banfield, Ann. 1982. Unspeakable Sentences. Boston: Routledge and Kegan Paul.Google Scholar
Bhatia, Vijay Kumar. 2004. Worlds of Written Discourse: A Genre-Based View. London: Continuum International.Google Scholar
Das, Sanjiv and Mike Chen. 2001. “Yahoo! for Amazon: Extracting Market Sentiment from Stock Message Boards.” In Proceedings of the Asia Pacific Finance Association Annual Conference APFA, 37–56.Google Scholar
Dave, Kushal, Steve Lawrence, and David M. Pennock. 2003. “Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews.” In Proceedings of WWW, 519–528. DOI logoGoogle Scholar
Dini, Luca and Giampaolo Mazzini. 2002. “Opinion Classification through Information Extraction.” in Proceedings of the Conference on Data Mining Methods and Databases for Engineering, Finance and Other Fields (Data Mining), 299–310.Google Scholar
Dotti, Fiorella Carla. 2013. “Overcoming Problems in Automated Appraisal Recognition: The Attitude System in Inscribed Appraisal.” Procedia-Social and Behavioral Sciences 95: 442–446. DOI logoGoogle Scholar
Eggins, Suzanne. 1994. An Introduction to Systemic Functional Linguistics. London: Pinter Publishers.Google Scholar
Eggins, Suzanne and James R. Martin. 1997. “Genres and registers of discourse”. In Discourse as Structure and Process. Discourse studies: A Multidisciplinary Introduction, ed. by T. A. van Dijk, 230–256. London: Sage. DOI logoGoogle Scholar
Eggins, Suzanne and Diana Slade. 1997. Analysing casual conversation. London: Cassell.Google Scholar
Google. (n.d.). Comment posting policy. Retrieved from <[URL]> (accessed on November, 2019).
Guzmán, Emitza, and Walid Maalej. 2014. “How do users like this feature? A fine grained sentiment analysis of app reviews.” In 22nd International Requirements Engineering Conference (RE). IEEE Press. DOI logoGoogle Scholar
Halliday, Michael Alexander Kirkwood, and Ruqaiya Hasan. 1985. Language, context, and text: Aspects of language in a social-semiotic perspective. Oxford: Oxford University Press.Google Scholar
Hasan, Ruqaiya. 1984. “The nursery tale as genre.” Nottingham Linguistics Circular 13. 71–102.Google Scholar
Khalid, Hammad. 2013. “On identifying user complaints of iOS apps.” In Proceedings of the 2013 International Conference on Software Engineering. IEEE Press. DOI logoGoogle Scholar
Khoo, Christopher Soo-Guan, Armineh Nourbakhsh, and Jin-Cheon Na. 2012. “Sentiment Analysis of Online News Text: A Case Study of Appraisal Theory.” Online Information Review 36 (6): 858–878. DOI logoGoogle Scholar
Liu, Bing. 2010. “Sentiment analysis and subjectivity.” Handbook of natural language processing 2: 627–666.Google Scholar
. 2012. “Sentiment analysis and opinion mining.” Synthesis Lectures on Human Language Technologies 5 (1): 1–167. DOI logoGoogle Scholar
Martin, James R. 1984. “Language, register and genre.” In Children Writing: Reader, ed. by F. Christie, 21–30. Geelong, Victoria: Deakin University Press.Google Scholar
1985. “Process and text: Two aspects of human semiosis.” In Systemic perspectives on discourse, ed. by James Benson and William Greaves, 248–274. Norwood, NJ: Ablex.Google Scholar
2000. Beyond exchange: Appraisal systems in English. In Evaluation in Text: Authorial Distance and the Construction of Discourse, ed. by Susan Hunston and Geoffrey Thompson, 142–175. Oxford: Oxford University Press.Google Scholar
Martin, James. R. and Peter R. White. 2005. The Language of Evaluation. Appraisal in English. New York: Palgrave. DOI logoGoogle Scholar
Mora López, Natalia. 2017. Annotating Appraisal in English and Spanish product reviews from mobile application stores a contrastive study for linguistic and computational purposes. Unpublished PhD dissertation. Available online at <[URL]>
Nasukawa, Tetsuya and Jeonghee Yi. 2003. “Sentiment Analysis: Capturing Favorability using Natural Language Processing.” In Proceedings of the Conference on Knowledge Capture (K-CAP). DOI logoGoogle Scholar
Nwogu, Kevin Ngozi. 1997. “The medical research paper: Structure and functions.” English for Specific Purposes 16 (2): 119–138. DOI logoGoogle Scholar
Pang, Bo, Lillian Lee, and Shivakumar Vaithyanathan. 2002. “Thumbs Up? Sentiment Classification using Machine Learning Techniques.” In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 79–86. DOI logoGoogle Scholar
Pollach, Irene. 2006. “Electronic word of mouth: a genre analysis of product reviews on consumer opinion web sites.” In Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS’06). IEEE Press. DOI logoGoogle Scholar
Skelton, John. 1994. “Analysis of the structure of original research papers: an aid to writing original papers for publication.” British Journal of General Practice 44: 455–459.Google Scholar
Swales, John. 1990. Genre Analysis. English in Academic and Research Settings. Cambridge: Cambridge University Press.Google Scholar
. 2004. Research Genres. New York: Cambridge University Press. DOI logoGoogle Scholar
Taboada, Maite. 2003. “Modeling task-oriented dialogue.” Computers and the Humanities 37 (4): 431–454. DOI logoGoogle Scholar
. 2004a. Building Coherence and Cohesion: Task-oriented Dialogue in English and Spanish. Amsterdam and Philadelphia: John Benjamins. DOI logoGoogle Scholar
. 2004b. “The genre structure of bulletin board messages.” Text Technology 13 (2): 55–82.Google Scholar
. 2011. “Stages in an online review genre.” Text & Talk-An Interdisciplinary Journal of Language, Discourse & Communication Studies 31 (2): 247–269. DOI logoGoogle Scholar
Taboada, Maite and Jack Grieve. 2004. “Analyzing Appraisal Automatically.” American Association for Artificial Intelligence Spring Symposium on Exploring Attitude and Affect in Text. Stanford. March 2004. AAAI Technical Report SS-04-07, 158–161.Google Scholar
Taboada, Maite, and Julia Lavid. 2003. “Rhetorical and thematic patterns in scheduling dialogues: A generic characterization.” Functions of Language 10 (2): 147–179. DOI logoGoogle Scholar
Taboada, Maite, Julian Brooke, and Manfred Stede. 2009. “Genre-Based Paragraph Classification for Sentiment Analysis.” In Proceedings of 10th Annual SIGDIAL Conference on Discourse and Dialogue. London, UK. September 2009, 62–70. DOI logoGoogle Scholar
Turney, Peter D. 2002. “Thumbs Up Or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews.” In Proceedings of the Association for Computational Linguistics (ACL), 417–424.Google Scholar
Vasa, Rajesh, Leonard Hoon, Kon Mouzakis, and Akihiro Noguchi. 2012. “A preliminary analysis of mobile app user reviews.” Proceedings of the 24th Australian Computer-Human Interaction Conference. ACM. DOI logoGoogle Scholar
Vásquez, Camilla. 2012. “Narrativity and involvement in online consumer reviews: The case of TripAdvisor.” Narrative Inquiry 22(1): 105–121. DOI logoGoogle Scholar
White, Peter R. 2003. “Beyond modality and hedging: A dialogic view of the language of intersubjective stance.” Text, 23(2): 259–284. DOI logoGoogle Scholar
Whitelaw, Casey, Navendu Garg, and Shlomo Argamon. 2005. “Using Appraisal Groups for Sentiment Analysis.” In Proceedings of the ACM SIGIR Conference on Information and Knowledge Management (CIKM), 625–631. DOI logoGoogle Scholar
Wiebe, Janyce M. 1994. “Tracking point of view in narrative.” Computational Linguistics 20 (2): 233–287.Google Scholar
Wiebe, Janyce M., Eric Breck, Chris Buckley, Claire Cardie, Paul Davis, Bruce Fraser, Diane J. Litman, David R. Pierce, Ellen Riloff, and Theresa Wilson. 2003. “Recognizing and Organizing Opinions Expressed in the World Press.” In Proceedings of the AAAI Spring Symposium on New Directions in Question Answering, 12–19.Google Scholar
Yu, Hong and Vasileios Hatzivassiloglou. 2003. “Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences.” In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 129–136. DOI logoGoogle Scholar