Sentiment analysis based on a social media customised dictionary

This article presents a methodology to classify the polarity of words from selected Tweets. Usually, social media sentiment (SMS) is lexically determined, manually or by machine learning. However, these methods are either slow or based on a pre-established dictionary, thus not providing a customised analysis. We propose a methodology that, after having mined the topic-related Tweets, filters relevant words based on the mean and standard deviation frequency in positive and negative market days to remove neutral terms. Subsequently, through an ad hoc perceptual mapping, we assign a polarity to the dataset. This method allows the building of a dictionary associated with the investor sentiment customised to that • The use of both statistical and perceptual map filters allows a specific asset dictionary to be built; • Textual sentiment analysis based on social media; • The proposed method efficiently overcomes generic dictionaries and language issues.