Seminar Users in the Arabic Twitter Sphere

We introduce the notion of "seminar users", who are social media users engaged in propaganda in support of a political entity. We develop a framework that can identify such users with 84.4% precision and 76.1% recall. While our dataset is from the Arab region, omitting language-specific features has only a minor impact on classification performance, and thus, our approach could work for detecting seminar users in other parts of the world and in other languages. We further explored a controversial political topic to observe the prevalence and potential potency of such users. In our case study, we found that 25% of the users engaged in the topic are in fact seminar users and their tweets make nearly a third of the on-topic tweets. Moreover, they are often successful in affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201

Aspect Level Sentiment Analysis using Machine Learning Approach: A Comprehensive Review

Sentimental analysis is now used from product marketing specific to the detection of social behavior. Progress on Facebook, Twitter, Youtube and other microblogging and social networking sites has not only contributed to a change in social sites, but also to the way we use these sites and the way we do it. People are fundamentally changing their feelings and their points of view with the general public. In this paper a detailed study of different approaches for lexicon-based sentiment analysis are discussed. This paper also shows that efficiency of machine learning over traditional lexicon based sentiment analysis

Investigating Predictive Power of Stock Micro Blog Sentiment in Forecasting Future Stock Price Directional Movement

This study attempts to discover and evaluate the predictive power of stock micro blog sentiment on future stock price directional movements. We construct a set of robust models based on sentiment analysis and data mining algorithms. Using 72,221 micro blog postings for 1909 stock tickers and 3874 distinct authors, our study reveals not only that stock micro blog sentiments do have predictive power for simple and market-adjusted returns respectively, but also that this predictive accuracy is consistent with the underreaction hypothesis observed in behavioral finance. We establish that stock micro blog with its succinctness, high volume and real-time features do have predictive power over future stock price movements. Furthermore, this study provides support for the model of irrational investor sentiment, recommends a supplementary investing approach using user-generated content and validates an instrument that may contribute to the monetization schemes for Virtual Investing Communities

Some experiments on modeling stock market behavior using investor sentiment analysis and posting volume from twitter

The analysis of microblogging data related with stock mar- kets can reveal relevant new signals of investor sentiment and attention. It may also provide sentiment and attention indicators in a more rapid and cost-effective manner than other sources. In this study, we created several indicators using Twitter data and investigated their value when model- ing relevant stock market variables, namely returns, trading volume and volatility. We collected recent data from nine ma jor technological companies. Several sentiment analy- sis methods were explored, by comparing 5 popular lexical resources and two novel lexicons (emoticon based and the merge of all 6 lexicons) and sentiment indicators produced using two strategies (based on daily words and individual tweet classifications). Also, we measured posting volume associated with tweets related to the analyzed companies. While a short time period is considered (32 days), we found scarce evidence that sentiment indicators can explain these stock returns. However, interesting results were obtained when measuring the value of using posting volume for fit- ting trading volume and, in particular, volatility.This work is funded by FEDER, through the program COM- PETE and the Portuguese Foundation for Science and Technology (FCT), within the project FCOMP-01-0124-FEDER- 022674

Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes

This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception

Automatic creation of stock market lexicons for sentiment analysis using StockTwits data

Sentiment analysis has been increasingly applied to the stock market domain. In particular, investor sentiment indicators can be used to model and predict stock market variables. In this context, the quality of the sentiment analysis is highly dependent of the opinion lexicon adopted. However, there is a lack of lexicons adjusted to microblogging stock market data. In this work, we propose an automatic procedure for the creation of such lexicon by exploring a large set of labeled messages from StockTwits, a popular financial microblogging service, and using four statistical measures: adaptations of the known TF-IDF, Information Gain, Class Percentage, and a newly proposed Weighted Class Probability. The obtained lexicons are competitive when compared with a set of six reference lexicons. Moreover, we verified that it is beneficial to use continuous sentiment scores instead of sentiment labels.We wish to thank StockTwits for kindly providing their data. This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: PEst-OE/EEI/UI0319/2014