A case study of Spanish text transformations for twitter sentiment analysis

Abstract

Sentiment analysis is a text mining task that determines the polarity of a given text, i.e., its positiveness or negativeness. Recently, it has received a lot of attention given the interest in opinion mining in micro-blogging platforms. These new forms of textual expressions present new challenges to analyze text given the use of slang, orthographic and grammatical errors, among others. Along with these challenges, a practical sentiment classifier should be able to handle efficiently large workloads.The aim of this research is to identify which text transformations (lemmatization, stemming, entity removal, among others), tokenizers (e.g., words n-grams), and tokens weighting schemes impact the most the accuracy of a classifier (Support Vector Machine) trained on two Spanish corpus. The methodology used is to exhaustively analyze all the combinations of the text transformations and their respective parameters to find out which characteristics the best performing classifiers have in common. Furthermore, among the different text transformations studied, we introduce a novel approach based on the combination of word based n-grams and character based q-grams. The results show that this novel combination of words and characters produces a classifier that outperforms the traditional word based combination by 11.17% and 5.62% on the INEGI and TASS'15 dataset, respectively.

Feature Fusion for Negation Scope Detection in Sentiment Analysis: Comprehensive Analysis over Social Media

“…Recent research has been focus over arithmetical techniques to discriminate explicit and implicit polarity shifts valuation. Tellez et al [21]use rule-based method to spot polarity shifts in explicit negations and contrasts. Ghiassi et al [14] use BOW to handle valence shifter such as intensifiers, diminishers and sarcasm.…”

“…Whereas as proposed feature fusion, Text feature extraction technique POS [24], [25], BOW [21], [16], [19], [13], [26], [27] and Hashtag help the overcome the limitation of conjunction word "AND" through grammatical marking, sentiment word and sarcasm identification respectively and simultaneously lead to evaluate polarity score of different part of sentence. (b) Punctuation Mark Identification: -Punctuation Mark (",", "!…”

“…Grammatical dependency parser build syntactic tree [28] and their lowest level are help to figure out scope of negation. Text feature extraction technique POS [24], [25], BOW [21], [16], [19], [13], [26], [27] and Hashtag help for grammatical marking lead to evaluate lowest level of grammatical syntactic relationship.…”

A framework based on Twitter and big data analytics to enhance sustainability performance

“…Therefore, one of the most significant types of data utilization from Twitter is the sentiment analysis. The simple definition for sentiment analysis is: text‐mining that determines the positiveness, negativeness, or neutrality within a specific trending subject (Pandey, Rajpoot, & Saraswat, ; Tellez et al., ). Thus, the sentiment analysis approach is already used in various contexts (Gaikar, Marakarkandy, & Dasgupta, ), such as analyzing the performance of Indian movies (Schumaker et al., ) and predicting wins and point spreads in the Premier League (England).…”

INGEOTEC at SemEval-2019 Task 5 and Task 6: A Genetic Programming Approach for Text Classification

“…Our approach takes into account several features mentioned above. For example, the effects of character-level n-grams are broadly studied for related tasks in (Tellez et al, 2017b). In particular, text modeling is a crucial factor in our approach; therefore we used the approach presented in (Tellez et al, 2018) that selects the best configuration on the datasets concerned.…”