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 organisation. A practical application was carried out to test the proposed methodology. The results were significant and in line with the behavioural finance theory, confirming that irrational investor feelings-expressed via social media-drive a portion of asset prices. Results also confirm the investor asymmetric behaviour under gain or loss scenarios, with the latter generating more impact than the former because people are risk-averse. The proposed method is expected to identify patterns of behaviour in social media linked to market oscillations, thereby contributing to risk management and optimising decision-making in the stock market.•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.
Keywords: Behavioural finance; Sentiment measurement; Sentiment perceptual map; Social media sentiment.
© 2021 The Author(s). Published by Elsevier B.V.