AWESSOME : An unsupervised sentiment intensity scoring framework using neural word embeddings

Htait, Amal and Azzopardi, Leif; Hiemstra, Djoerd and Moens, Marie-Francine and Mothe, Josiane and Perego, Raffaele and Potthast, Martin and Sebastiani, Fabrizio, eds. (2021) AWESSOME : An unsupervised sentiment intensity scoring framework using neural word embeddings. In: Advances in Information Retrieval - 43rd European Conference on IR Research, ECIR 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12657 . Springer, ITA, pp. 509-513. ISBN 9783030722401

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    Abstract

    Sentiment analysis (SA) is the key element for a variety of opinion and attitude mining tasks. While various unsupervised SA tools already exist, a central problem is that they are lexicon-based where the lexicons used are limited, leading to a vocabulary mismatch. In this paper, we present an unsupervised word embedding-based sentiment scoring framework for sentiment intensity scoring (SIS). The framework generalizes and combines past works so that pre-existing lexicons (e.g. VADER, LabMT) and word embeddings (e.g. BERT, RoBERTa) can be used to address this problem, with no require training, and while providing fine grained SIS of words and phrases. The framework is scalable and extensible, so that custom lexicons or word embeddings can be used to core methods, and to even create new corpus specific lexicons without the need for extensive supervised learning and retraining. The Python 3 toolkit is open source, freely available from GitHub (https://github.com/cumulative-revelations/awessome ) and can be directly installed via pip install awessome.

    ORCID iDs

    Htait, Amal ORCID logoORCID: https://orcid.org/0000-0003-4647-9996 and Azzopardi, Leif; Hiemstra, Djoerd, Moens, Marie-Francine, Mothe, Josiane, Perego, Raffaele, Potthast, Martin and Sebastiani, Fabrizio