eprintid: 29715 rev_number: 8 eprint_status: archive userid: 19147 dir: disk0/00/02/97/15 datestamp: 2019-04-24 09:50:07 lastmod: 2021-04-02 15:59:23 status_changed: 2019-04-24 09:50:07 type: article metadata_visibility: show creators_name: Salas-Zárate, María Del Pilar creators_name: López-López, Estanislao creators_name: Valencia-Garcia, Rafael creators_name: Aussenac-Gilles, Nathalie creators_name: Almela, Ángela creators_name: Alor-Hernández, Giner creators_idrefppn: 034874984 title: A study on LIWC categories for opinion mining in Spanish reviews ispublished: pub subjects: subjects_INFO abstract: With the exponential growth of social media i.e. blogs and social networks, organizations and individual persons are increasingly using the number of reviews of these media for decision making about a product or service. Opinion mining detects whether the emotion of an opinion expressed by a user on Web platforms in natural language, is positive or negative. This paper presents extensive experiments to study the effectiveness of the classification of Spanish opinions in five categories: highly positive, highly negative, positive, negative and neutral, using the combination of the psychological and linguistic features of LIWC. LIWC is a text analysis software that enables the extraction of different psychological and linguistic features from natural language text. For this study, two corpora have been used, one about movies and one about technological products. Furthermore, we have conducted a comparative assessment of the performance of various classification techniques: J48, SMO and BayesNet, using precision, recall and F-measure metrics. All in all, findings have revealed that the positive and negative categories provide better results than the other categories. Finally, experiments on both corpora indicated that SMO produces better results than BayesNet and J48 algorithms obtaining an F-measure of 90.4% and 87.2% in each domain. date: 2014 date_type: published publisher: SAGE Publications (UK and US) faculty: info divisions: IRIT keywords: Sentiment Analysis - Opinion Mining - Natural Language Processing With LIWC - Machine Learning language: en has_fulltext: TRUE view_date_year: 2014 full_text_status: public publication: Journal of Information Science volume: 40 number: 6 pagerange: 1-13 refereed: TRUE issn: 0165-5515 harvester_local_overwrite: eprintid harvester_local_overwrite: userid harvester_local_overwrite: date harvester_local_overwrite: official_url harvester_local_overwrite: issn harvester_local_overwrite: dir harvester_local_overwrite: keywords harvester_local_overwrite: pagerange harvester_local_overwrite: publisher harvester_local_overwrite: volume harvester_local_overwrite: creators_name harvester_local_overwrite: faculty harvester_local_overwrite: site harvester_local_overwrite: abstract harvester_local_overwrite: title harvester_local_overwrite: publication harvester_local_overwrite: type harvester_local_overwrite: number harvester_local_overwrite: note harvester_local_overwrite: ispublished harvester_local_overwrite: id_number harvester_local_overwrite: event_title harvester_local_overwrite: pres_type harvester_local_overwrite: event_location harvester_local_overwrite: series harvester_local_overwrite: isbn harvester_local_overwrite: book_title harvester_local_overwrite: editors_name harvester_local_overwrite: department harvester_local_overwrite: thesis_type harvester_local_overwrite: pages harvester_local_overwrite: place_of_pub harvester_local_overwrite: divisions harvester_local_overwrite: subjects harvester_local_overwrite: date_type harvester_local_overwrite: language harvester_local_overwrite: refereed harvester_local_overwrite: creators_idrefppn site: ut1 citation: Salas-Zárate, María Del Pilar, López-López, Estanislao, Valencia-Garcia, Rafael, Aussenac-Gilles, Nathalie , Almela, Ángela and Alor-Hernández, Giner (2014) A study on LIWC categories for opinion mining in Spanish reviews. Journal of Information Science, 40 (6). pp. 1-13. document_url: https://publications.ut-capitole.fr/id/eprint/29715/1/assistant_3105901_575374099_0.pdf