On using Twitter to monitor political sentiment and predict election results

The body of content available on Twitter undoubtedly contains a diverse range of political insight and commentary. But, to what extent is this representative of an electorate? Can we model political sentiment effectively enough to capture the voting intentions of a nation during an election capaign? We use the recent Irish General Election as a case study for investigating the potential to model political sentiment through mining of social media. Our approach combines sentiment analysis using supervised learning and volume-based measures. We evaluate against the conventional election polls and the final election result. We find that social analytics using both volume-based measures and sentiment analysis are predictive and wemake a number of observations related to the task of monitoring public sentiment during an election campaign, including examining a variety of sample sizes, time periods as well as methods for qualitatively exploring the underlying content

Sentiment analysis on online social network

A large amount of data is maintained in every Social networking sites.The total data constantly gathered on these sites make it difficult for methods like use of field agents, clipping services and ad-hoc research to maintain social media data. This paper discusses the previous research on sentiment analysis

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

Reliable Sentiment Analysis in Social Media

University of Technology Sydney. Faculty of Engineering and Information Technology.Sentiment analysis in social media is critical yet challenging because the source materials (i.e., reviews posted in social media) are with high complexity, low quality, and uncertain credibility. For example, words and sentences in a textual review may couple with each other, and they may have heterogeneous meanings under different contexts or in different language locales. These couplings and heterogeneities essentially determine the sentiment polarity of the review but are too complex to be captured and modeled. Also, social reviews contain a large number of informal words and typos (a.k.a., noise) but a rare number of vocabularies (a.k.a., sparsity). As a result, most of the existing natural language processing (NLP) methods may fail to represent social reviews effectively. Furthermore, a large proportion of social reviews are posted by fraudsters. These fraud reviews manipulate social opinion, and thus, they disturb sentiment analysis. This research focuses on reliable sentiment analysis in social media. It systematically investigates the sentiment analysis techniques to tackle three major challenges in social media: high data complexity, low data quality, and uncertain credibility. Specifically, this research focuses on two research problems: general sentiment analysis in social media and fraudulent sentiment analysis in social media. The general sentiment analysis targets on tackling high data complexity and low-quality of social articles that are credible. The fraudulent sentiment analysis handles the uncertain credibility issue, which is common and profoundly affects the precise sentiment analysis in social media. Based on these investigations, this research proposes a serial of methods to achieve reliable sentiment analysis: It studies the polarity-shift characteristics and non-IID characteristics in general paragraphs to capture the sentiment more accurately. It further models multi-granularity noise and sparsity in short text, which is the most common data in social media, for robust short text sentiment analysis. Finally, it tackles the uncertain credibility problem in social media by studying fraudulent sentiment analysis in both supervised and unsupervised scenarios. This research evaluates the performance and properties of the proposed reliable sentiment analysis methods by extensive experiments on large real-world data sets. It demonstrates that the proposed methods are superior and reliable in social media sentiment analysis

Using sentiment analysis technique for analyzing Thai customer satisfaction from social media

With the rapidly increasing number of Thai online customer reviews available in social media and websites, sentiment analysis technique, also called opinion mining, has become an important task in the past few years.This technique aims to analyze people’s emotions, opinion, attitudes and sentiments.The classical approaches for opinion mining represents the reviews as bag-of-words as many words can be used to identify positive or negative feedbacks.This makes these methods work well with European language reviews which are segmented texts.However, these bag-of-word based methods face problem with Thai customer’s review which is non-segmented text, since Thai texts are formed as a long sequence of characters without word boundaries.Up to now, not much research conducted on sentiment analysis for Thai customer reviews.This paper proposes a sentiment analysis technique for Thai customer’s reviews.The proposed technique is based on the integration of Thai word extraction and sentiment analysis techniques for mining Thai customer’s opinion. To demonstrate the proposed technique, experimental studies on analyzing Thai customer’s reviews from social media are presented in this paper.The results show that the proposed method provides significant benefits for mining Thai customer’s opinion from social media