LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity Prediction

Abstract

In this paper, we present our contribution in SemEval2016 task7 1 : Determining Sentiment Intensity of English and Arabic Phrases, where we use web search engines for English and Arabic unsupervised sentiment intensity prediction. Our work is based, first, on a group of classic sentiment lexicons (e.g. Sen-timent140 Lexicon, SentiWordNet). Second, on web search engines' ability to find the cooccurrence of sentences with predefined negative and positive words. The use of web search engines (e.g. Google Search API) enhance the results on phrases built from opposite polarity terms.

English Sentiment Classification using Only the Sentiment Lexicons with a JOHNSON Coefficient in a Parallel Network Environment

“…There are the works related to PMI measure in (Bai et al, 2014;Turney and Littman, 2002;Malouf and Mullen, 2017;Scheible, 2010;Jovanoski et al, 2015;Htait et al, 2016;Wan et al, 2009;Brooke et al, 2009;Jiang et al, 2015;Brooke et al, 2009;Hernández-Ugalde et al, 2011;Ponomarenko et al, 2002;Meyer et al, 2004;Mladenović Drinić et al, 2008;Tamás et al, 2001). In the research (Bai et al, 2014), the authors generate several Norwegian sentiment lexicons by extracting sentiment information from two different types of Norwegian text corpus, namely, news corpus and discussion forums.…”

“…Overview of identifying the valence and the polarity of one term in English using a JOHNSON Coefficient (JC) Bai et al (2014;Turney and Littman, 2002;Malouf and Mullen, 2017;Scheible, 2010;Jovanoski et al, 2015;Htait et al, 2016;Wan et al, 2009;Brooke et al, 2009) the positive and the negative of Equation 2 in English are: Positive = {good, nice, excellent, positive, fortunate, correct, superior} and negative = {bad, nasty, poor, negative, unfortunate, wrong, inferior}.…”

“…The AltaVista search engine is used in the PMI equations of (Turney and Littman, 2002;Malouf and Mullen, 2017;Jovanoski et al, 2015) and the Google search engine is used in the PMI equations of (Scheible, 2010;Htait et al, 2016;Brooke et al, 2009). Besides, (Scheible, 2010) also uses German, (Jovanoski et al, 2015) also uses Macedonian, (Htait et al, 2016) also uses Arabic, (Wan et al, 2009) also uses Chinese and (Brooke et al, 2009) also uses Spanish. In addition, the Bing search engine is also used in (Htait et al, 2016).…”

Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets

“…UWB used Gaussian Regression (Hercig et al, 2016), while iLab-Edinburgh went in for linear regression (Refaee and Rieser, 2016). Team LSIS (Htait et al, 2016) had a completely unsupervised approach, using sentiment lexicons and PMI scores.…”

Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

“…UWB used Gaussian Regression , while iLab-Edinburgh went in for linear regression (Refaee and Rieser, 2016). Team LSIS (Htait et al, 2016) had a completely unsupervised approach, using sentiment lexicons and PMI scores.…”