Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining

By Soujanya Poria, Alexander Gelbukh, Amir Hussain, N. Howard, Dipankar Das, Sivaji Bandyopadhyay. IEEE Intelligent Systems, 2013

509 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/8e65b09bf17d0b89ee7b60db47cc830f1951b783

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In Artificial Intelligence

Citations in the corpus

Sentic patterns: Dependency-based rules for concept-level sentiment analysis (2014, methodology): “…semantic parser for deconstructing text into natural language concepts (Section 3.1), which then get represented and analyzed through a vector space of common-sense knowledge (Section 3.2) by means of an emotion categorization model (Section 3.3) and an ANN-based clustering technique (Section 3.4).”
Opinion Mining and Information Fusion: A survey (2016, methodology): “This resource is improved in [70], where it is enriched with affective information by fusing it with WordNet- Affect [71], another semantic resource, to add emotion labels such as Anger, Disgust, Joy and Surprise.”
Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges (2023, unknown): “[153] 5,732 positive, negative, neutral, and ambiguous”
News impact on stock price return via sentiment analysis (2014, unknown): “[31] enhance the SenticNet 1.”
Emotionally-Aware Chatbots: A Survey (2019, methodology): “Work by [42] added emotion label by mapping WordNet-Affect label to the”
SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis (2014, methodology): “…Gindl, and Scharl 2013); statistical methods, which calculate the valence of word co-occurrence frequencies on the base of a large training corpus (Poria et al. 2013); and concept-level approaches, which make use of semantic networks to infer conceptual and affective information conveyed by…”
Soft Similarity and Soft Cosine Measure: Similarity of Features in Vector Space Model (2014, background): “In natural language processing, text similarity plays crucial role in many tasks from plagiarism detection [18] and question answering [3] to sentiment analysis [14-16].”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, background): “S# Tasks and applications #Articles References 1 Subjectivity Classification 6 [44], [75], [110], [163], [167], [174] 2 Polarity determination 43 [12], [26], [29], [32], [33], [35], [40], [45], [48], [50], [54], [57], [66], [85], [95], [96], [108], [109], [112], [114], [123], [126], [154], [156], [1”