and its implementation in the form of a webbased application that can quantify sentiment contained in Twitter feeds and track the sentiment as it changes over time. Results are provided that evaluate the tool for use in performing tracking of sentiment analysis as perceptions change over time. post

and token-based association to handle topic specific sen-timent in subtask C. Our strategy has been to identify subphrases relevant to the designated topic/target and assign sentiment according to our subtask A classifier. Our submitted subtask A classifier ranked fourth in the Se-mEval official results

Sarcasm is a form of speech act in which the speakers convey their message in an implicit way. The inherently ambiguous nature of sarcasm sometimes makes it hard even for humans to decide whether an utterance is sarcastic or not. Recognition of sarcasm can benefit many sentiment analysis NLP

We address the problem of performing po-larity classification on Twitter over differ-ent languages, focusing on English and Spanish, comparing three techniques: (1) a monolingual model which knows the language in which the opinion is written, (2) a monolingual model that acts based on the decision

Abstract. This paper addresses the issue of how personality recognition can be helpful for sentiment analysis. We exploited the corpus for sentiment analysis released for the SEMEVAL 2013, we automatically annotated personality labels by means of an unsupervised system for personality recognition

, political and economic environment thus helps in better decision making. Most of the recent studies are bounded to be analyzing and predicting public sentiments. In this paper, we have done further analysis to know the useful insights for PSV using tweets and Facebook comments with a specific target. We

and the applications of using a Twitter and Google maps mashup. Further, we analyze the sentiments expressed within a particular sentence, paragraph or document etc. The analysis based on sentiments can pave way for automatic trend analysis, topic recognition and opinion mining etc. Furthermore, we can fairly estimate

Abstract A common form of sarcasm on Twitter consists of a positive sentiment contrasted with a negative situation. For example, many sarcastic tweets include a positive sentiment, such as "love" or "enjoy", followed by an expression that describes an undesirable activity

and are powered by different forum software packages, they always have similar implicit navigation paths connected by specific URL types to lead users from entry pages to thread pages. Based on this observation, the web forum crawling problem is reduced to a URL-type recognition problem and classifies them

awareness. Thus, tweets of first sense of irony may attack a specific target, and the speaker may tag his/her tweet irony because the tweet itself is ironic. These tweets though tagged as irony are in fact sarcastic tweets. Different from this, the tweets of second sense of irony is tagged to classify