Twitter Sentimental Analysis
This paper introduces a word embedding (Word2Vec) technique obtained by unsupervised learning built on large twitter corpora, this process uses co-occurrence statistical characteristics between words in tweets and hidden contextual semantic interrelation.
Abstract
Twitter sentiment analysis (TSA) provides the methods to survey public emotions about the products or events associated with them. Categorization of opinions through tweets involves a great scope of study and may yield interesting results and insights on public opinion and social behavior towards different events, services, product, geopolitical issues, situations and scenarios that concern mankind at large. These attributes are expressed explicitly through emoticons, exclamation, sentiment words and so on. In this paper, we introduce a word embedding (Word2Vec) technique obtained by unsupervised learning built on large twitter corpora, this process uses co-occurrence statistical characteristics between words in tweets and hidden contextual semantic interrelation