Enhanced Twitter Sentiment Classification Using Contextual Information

The rise in popularity and ubiquity of Twitter has made sentiment analysis of tweets an important and well-covered area of research. However, the 140 character limit imposed on tweets makes it hard to use standard linguistic methods for sentiment classification. On the other hand, what tweets lack in structure they make up with sheer volume and rich metadata. This metadata includes geolocation, temporal and author information. We hypothesize that sentiment is dependent on all these contextual factors. Different locations, times and authors have different emotional valences. In this paper, we explored this hypothesis by utilizing distant supervision to collect millions of labelled tweets from different locations, times and authors. We used this data to analyse the variation of tweet sentiments across different authors, times and locations. Once we explored and understood the relationship between these variables and sentiment, we used a Bayesian approach to combine these variables with more standard linguistic features such as n-grams to create a Twitter sentiment classifier. This combined classifier outperforms the purely linguistic classifier, showing that integrating the rich contextual information available on Twitter into sentiment classification is a promising direction of research.Twitter (Firm

A context based model for sentiment analysis in twitter for the italian language

Studi recenti per la Sentiment Analysis in Twitter hanno tentato di creare modelli per caratterizzare la polarit´a di un tweet osservando ciascun messaggio in isolamento. In realt`a, i tweet fanno parte di conversazioni, la cui natura pu`o essere sfruttata per migliorare la qualit`a dell’analisi da parte di sistemi automatici. In (Vanzo et al., 2014) `e stato proposto un modello basato sulla classificazione di sequenze per la caratterizzazione della polarit` a dei tweet, che sfrutta il contesto in cui il messaggio `e immerso. In questo lavoro, si vuole verificare l’applicabilit`a di tale metodologia anche per la lingua Italiana.Recent works on Sentiment Analysis over Twitter leverage the idea that the sentiment depends on a single incoming tweet. However, tweets are plunged into streams of posts, thus making available a wider context. The contribution of this information has been recently investigated for the English language by modeling the polarity detection as a sequential classification task over streams of tweets (Vanzo et al., 2014). Here, we want to verify the applicability of this method even for a morphological richer language, i.e. Italian

Semantic Sentiment Analysis of Twitter Data

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about the Brexit? Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages. Here we present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition. 201

A Linked Open Data Approach for Sentiment Lexicon Adaptation

Social media platforms have recently become a gold mine for organisations to monitor their reputation by extracting and analysing the sentiment of the posts generated about them, their markets, and competitors. Among the approaches to analyse sentiment from social media, approaches based on sentiment lexicons (sets of words with associated sentiment scores) have gained popularity since they do not rely on training data, as opposed to Machine Learning approaches. However, sentiment lexicons consider a static sentiment score for each word without taking into consideration the different contexts in which the word is used (e.g, great problem vs. great smile). Additionally, new words constantly emerge from dynamic and rapidly changing social media environments that may not be covered by the lexicons. In this paper we propose a lexicon adaptation approach that makes use of semantic relations extracted from DBpedia to better understand the various contextual scenarios in which words are used. We evaluate our approach on three different Twitter datasets and show that using semantic information to adapt the lexicon improves sentiment computation by 3.7% in average accuracy, and by 2.6% in average F1 measure