From Public Polls to Tweets: Developing an Algorithm for Classifying Sentiment from Twitter Based on Computing with Words

Abstract

Abstract:Uncertainty is an intrinsic part of sentiment analysis, especially when dealing with social media (Twitter data) that known as noisy texts. Although there are many researches have been done in sentiment analysis, accuracy in identifying sentiment from Twitter data is still far from satisfactory. In this paper, we address this limitation by investigating computing with words (CWW) and granule computing (GC) approaches that all contribute towards improving sentiment classification on Twitter. CWW can provide a solid basis for the computational theory of perceptions under the environments of imprecision, uncertainty, and partial truth. CWW technique is employed to translate propositions expressed in a natural language into what is called generalized constraint language (GCL) with possibilistic type and applying rules of fuzzy constraint propagation. GC is engaged to infer an answer to a query expressed in a natural language "which granule does the proposition belongs to?" The experimental results show that it is feasible to use CWW to classify sentiment.

Modified fuzzy sentiment analysis approach based on user ranking suitable for online social networks

“…Plentiful articles in SA are annually delivered [4, 6]. In consonance with them, techniques of SA are classified as machine learning approach (supervised and unsupervised), lexicon‐based approach (e.g.…”

“…For NLP, two main tasks are executed as follows [5, 6, 16]: Tokenisation, the lexical analysis uses tokenisation to break the whole text into words, phrases etc. referred to as tokens. POS, which allocates parts of speech to every single word (i.e.…”

“…Applying Fuzzy logic technique, along with natural language processing (NLP) can fulfil this necessity [5]. In light of existing researches, Fuzzy logic techniques are not popular in the field of SA, even though Fuzzy techniques can improve the process with their weight‐ages and strengths; which can end in a more accurate analysis [5, 6].…”