AWESSOME: An Unsupervised Sentiment Intensity Scoring Framework Using Neural Word Embeddings

Amal Htait, Leif Azzopardi

Research output: Chapter in Book/Published conference outputChapter


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.
Original languageUndefined/Unknown
Title of host publicationEuropean Conference on Information Retrieval
Subtitle of host publicationAdvances in Information Retrieval
Number of pages5
Publication statusPublished - 30 Mar 2021
Event43rd European Conference on IR Research - Virtual - online
Duration: 28 Mar 20211 Apr 2021

Publication series

NameLecture Notes in Computer Science (LNCS)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference43rd European Conference on IR Research
Abbreviated titleECIR 2021

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