Sentiment analysis on Twitter data has attracted much at-tention recently. One of the system’s key features, is the immediacy in communication with other users in an easy, user-friendly and fast way. Consequently, people tend to express their feelings freely, which makes Twitter an ideal source

We connect measures of public opinion measured from polls with sentiment measured from text. We analyze several surveys on consumer confidence and political opinion over the 2008 to 2009 period, and find they correlate to sentiment word frequencies in contemporaneous Twitter messages. While our

We present work on sentiment analysis in Twitter for Macedonian. As this is pio-neering work for this combination of lan-guage and genre, we created suitable re-sources for training and evaluating a sys-tem for sentiment analysis of Macedonian tweets. In particular, we developed a cor-pus of tweets

We perform a sentiment analysis of all tweets published on the microblogging platform Twitter in the second half of 2008. We use a psychometric instrument to ex-tract six mood states (tension, depression, anger, vigor, fatigue, confusion) from the aggregated Twitter con-tent and compute a six

MapReduce and its variants have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However, most of these systems are built around an acyclic data flow model that is not suitable for other popular applications. This paper focuses on one such class

of gauging public opinion for business marketing or social studies. However, Twitter's unique characteristics give rise to new problems for current sentiment analysis methods, which originally focused on large opinionated corpora such as product reviews. In this paper, we propose a new entity

News can be good or bad, but it is seldom neutral. Although full comprehension of natural language text remains well be-yond the power of machines, the statistical analysis of rela-tively simple sentiment cues can provide a surprisingly mean-

. Specifically, we develop three neural networks to effectively incorporate the supervision from sentiment polarity of text (e.g. sentences or tweets) in their loss functions. To obtain large scale training corpora, we learn the sentiment-specific word embedding from massive distant-supervised tweets collected

not predict retweetability of a user’s tweet. We believe that this research would inform the design of sensemaking and analytics tools for social media streams. Keywords-Twitter; retweet; tweet; follower; social network; social media; factor analysis I.

. Specif-ically, we develop three neural networks to effectively incorporate the supervision from sentiment polarity of text (e.g. sen-tences or tweets) in their loss function-s. To obtain large scale training corpora, we learn the sentiment-specific word em-bedding from massive distant-supervised tweets