Meta-level sentiment models for big social data analysis

Abstract

People react to events, topics and entities by expressing their personal opinions and emotions. These reactions can correspond to a wide range of intensities, from very mild to strong. An adequate processing and understanding of these expressions has been the subject of research in several fields, such as business and politics. In this context, Twitter sentiment analysis, which is the task of automatically identifying and extracting subjective information from tweets, has received increasing attention from the Web mining community. Twitter provides an extremely valuable insight into human opinions, as well as new challenging Big Data problems. These problems include the processing of massive volumes of streaming data, as well as the automatic identification of human expressiveness within short text messages. In that area, several methods and lexical resources have been proposed in order to extract sentiment indicators from natural language texts at both syntactic and semantic levels. These approaches address different dimensions of opinions, such as subjectivity, polarity, intensity and emotion. This article is the first study of how these resources, which are focused on different sentiment scopes, complement each other. With this purpose we identify scenarios in which some of these resources are more useful than others. Furthermore, we propose a novel approach for sentiment classification based on meta-level features. This supervised approach boosts existing sentiment classification of subjectivity and polarity detection on Twitter. Our results show that the combination of meta-level features provides significant improvements in performance. However, we observe that there are important differences that rely on the type of lexical resource, the dataset used to build the model, and the learning strategy. Experimental results indicate that manually generated lexicons are focused on emotional words, being very useful for polarity prediction. On the other hand, lexicons generated with automatic methods include neutral words, introducing noise in the detection of subjectivity. Our findings indicate that polarity and subjectivity prediction are different dimensions of the same problem, but they need to be addressed using different subspace features. Lexicon-based approaches are recommendable for polarity, and stylistic part-of-speech based approaches are meaningful for subjectivity. With this research we offer a more global insight of the resource components for the complex task of classifying human emotion and opinion.

A hybrid approach to the sentiment analysis problem at the sentence level

“…Other articles worth mentioning explore topics around sentiment lexicon-based techniques, like the contributions of Cho et al [14] and Huang et al [31]. The work by Bravo-Márquez et al [5], on the use of multiple techniques and tools in SA, offers a complete study on how several resources that "are focused on different sentiment scopes" can complement each other. The authors focus the discussion on methods and lexical resources that aid in extracting sentiment indicators from natural languages in general.…”

Conceptualizing Big Social Data

“…In general, researchers have focused on the analytics and utilization, having paid little attention to clarifying the very concept of BSD and understanding the related phenomena (for example, [19][20][21]). …”

THU_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji Prediction

“…These sentiment features are extracted using AffectiveTweets 4 (Mohammad and Bravo-Marquez, 2017) package in Weka 5 . Two filters are involved, i.e., TweetToLexiconFeatureVector (Bravo-Marquez et al, 2014) and TweetToSentiStrengthFeatureVector (Thelwall et al, 2012). These sentiment features are combined with the hidden tweet representations generate by neural networks to form the final feature representation of tweets.…”