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

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

7 Downloads (Pure)


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 and can be directly installed via
pip install awessome.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science
EditorsDjoerd Hiemstra, Marie-Francine Moens, Josiane Mothe, Raffaele Perego, Martin Potthast, Fabrizio Sebastiani
Place of PublicationCham, Switzerland
Number of pages5
ISBN (Electronic)9783030722401
ISBN (Print)9783030722395
Publication statusPublished - 28 Mar 2021
EventEuropean Conference on Information Retrieval 2021 - Online, Lucca, Italy
Duration: 28 Mar 20211 Apr 2021
Conference number: 43

Publication series

NameLecture Notes in Computer Science (LNCS)
PublisherSpringer International
ISSN (Print)0302-9743


ConferenceEuropean Conference on Information Retrieval 2021
Abbreviated titleECIR 2021
Internet address


  • sentiment intensity
  • pre-trained language model
  • lexicon
  • BERT

Fingerprint Dive into the research topics of 'AWESSOME: An unsupervised sentiment intensity scoring framework using neural word embeddings'. Together they form a unique fingerprint.

Cite this