Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics’ feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features ...

Recently, sentiment analysis has received a lot of attention due to the interest in mining opinions of social media users. Sentiment analysis consists in determining the polarity of a given text, i.e., its degree of positiveness or negativeness. Traditionally, Sentiment Analysis algorithms have been tailored to a specific language given the complexity of having a number of lexical variations an...

With the spurt of online user-generated contents on web, sentiment analysis has become a very active research issue in data mining and natural language processing. As the most important indicator of sentiment, sentiment words which convey positive and negative polarity are quite instrumental for sentiment analysis. However, most of the existing methods for identifying polarity of sentiment word...

In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter. This was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years. This year’s shared task competition consisted of five sentiment prediction subtasks. Two were reruns from previous years: (A) sentiment expressed by a phr...

This paper introduces a new general-purpose sentiment lexicon called the WKWSCI Sentiment Lexicon and compares it with three existing lexicons. The WKWSCI Sentiment Lexicon is based on the 6of12dict lexicon, and currently covers adjectives, adverbs and verbs. The words were manually coded with a value on a 7-point sentiment strength scale. The effectiveness of the four sentiment lexicons for se...

Domain adaptation is an important technology to handle domain dependence problem in sentiment analysis field. Existing methods usually rely on sentiment classifiers trained in source domains. However, their performance may heavily decline if the distributions of sentiment features in source and target domains have significant difference. In this paper, we propose an active sentiment domain adap...

Supervised approaches have proved their significance in sentiment analysis task, but they are limited to the languages, which have sufficient amount of annotated corpus. Hindi is a language, which is spoken by 4.70% of the world population, but it lacks a sufficient amount of annotated corpus for natural language processing tasks such as Sentiment Analysis (SA). With the increase in demand and ...

Sentiment Analysis is a task that still has several opened challenges. One of those challenges is the treatment of the negation, because a negative opinion can be built using negated positive words. Negation is a particular feature of each language, thus it must be considered differently per each language. In this article is shown a linguistic approach for the negation scope identification with...

The big amount of content generated from Internet users creates opportunities and challenges to researchers interested in the problem of sentiment analysis. All the information available regarding user’s opinions on different topics represent a great opportunity for automatic sentiment classification systems. However, user’s interest change very