Contextual semantics for sentiment analysis of Twitter

By Hassan Saif, Yulan He, Miriam Fernández, Harith Alani. Information Processing & Management, 2016

424 citations recorded by the source corpus.

We propose a semantic sentiment representation of words called SentiCircle.SentiCircle captures the contextual semantic of words from their co-occurrences.SentiCircle updates the sentiment of words based on their contextual semantics.SentiCircle can be used to perform entity- and tweet-level level sentiment analysis. Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4-5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/b060df8710ce6178e76524c9f0a03718fc7bf7d1

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In Artificial Intelligence

Citations in the corpus

KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media (2020, background): “Solutions to this problem vary from manual control to rule-based ﬁltering systems; however, these methods are time-consuming or prone to errors if the full context is not taken into consideration while assessing the sentiment of the text (Saif et al., 2016).”
Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews (2019, methodology): “A smaller value of subjectivity indicates that more objective words are used to describe products and services instead of revealing emotions or evaluating (Giatsoglou et al., 2017; Saif et al., 2016).”
A survey on classification techniques for opinion mining and sentiment analysis (2017, methodology): “2014; Jeyapriya and Selvi 2015), and recently micro-blogs (Balahur and Perea-Ortega 2015; Feng et al. 2015; Pandarachalil et al. 2015; Da Silva et al. 2016; Saif et al. 2016; Wu et al. 2016; Ma et al. 2017; Li et al. 2017; Keshavarz and Abadeh 2017; Huang et al. 2017).”
Deep Convolution Neural Networks for Twitter Sentiment Analysis (2018, methodology): “[42] proposed Hdb approach, the method SentiStrength in [43] and the Updated+Expanded in [44].”
Like It or Not (2016, methodology): “The dataset has been used for TSA [Saif et al. 2014, 2016].”