A lightweight algorithm for the emotional classification of crowdsourced venue reviews

Finding emotions in text is an area of research with wide-ranging applications. Analysis of sentiment in text can help determine the opinions and affective intent of writers, as well as their attitudes, evaluations and inclinations with respect to various topics. Previous work in sentiment analysis has been done on a variety of text genres, including product and movie reviews, news stories, editorials and opinion articles, or blogs. We describe a lightweight emotion annotation algorithm for identifying emotion category & intensity in reviews written by social media (Foursquare) users. The algorithm is evaluated against human subject performance and is found to compare favourably. This work opens up opportunities for solving the problem of helping user navigate through the plethora of venue reviews in mobile and desktop applications.

[1]  Kjetil Nørvåg,et al.  A study of opinion mining and visualization of hotel reviews , 2012, IIWAS '12.

[2]  Maulahikmah Galinium,et al.  Automatic mood classification of Indonesian tweets using linguistic approach , 2013, 2013 International Conference on Information Technology and Electrical Engineering (ICITEE).

[3]  Elizabeth Furtado,et al.  How Do Users Express Their Emotions Regarding the Social System in Use? A Classification of Their Postings by Using the Emotional Analysis of Norman , 2014, HCI.

[4]  Enrique Herrera-Viedma,et al.  Emotional Profiling of Locations Based on Social Media , 2015, ITQM.

[5]  R. Rajasree,et al.  Sentiment analysis in twitter using machine learning techniques , 2013, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[6]  Mitsunori Matsushita,et al.  Relationship between Emotional Words and Emoticons in Tweets , 2012, 2012 Conference on Technologies and Applications of Artificial Intelligence.

[7]  Stefan Stieglitz,et al.  Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior , 2013, J. Manag. Inf. Syst..

[8]  Saif Mohammad,et al.  #Emotional Tweets , 2012, *SEMEVAL.

[9]  Athena Vakali,et al.  Emotional Aware Clustering on Micro-blogging Sources , 2011, ACII.

[10]  Xueqi Cheng,et al.  Adaptive co-training SVM for sentiment classification on tweets , 2013, CIKM.

[11]  Sanjeev Ahuja,et al.  Sentiment analysis of movie reviews: A study on feature selection & classification algorithms , 2016, 2016 International Conference on Microelectronics, Computing and Communications (MicroCom).

[12]  Luis Alfonso Ureña López,et al.  Polarity classification for Spanish tweets using the COST corpus , 2015, J. Inf. Sci..

[13]  Zhu Wang,et al.  A sentiment-enhanced personalized location recommendation system , 2013, HT.

[14]  Pablo Gervás,et al.  A Joint Model of Feature Mining and Sentiment Analysis for Product Review Rating , 2011, ECIR.

[15]  Mike Thelwall,et al.  The role of emotional variables in the classification and prediction of collective social dynamics , 2014, ArXiv.

[16]  Ellen Riloff,et al.  Learning Emotion Indicators from Tweets: Hashtags, Hashtag Patterns, and Phrases , 2014, EMNLP.