Abstract
The sentiment analysis task has been traditionally divided into lexicon or machine learning approaches, but recently the use of word embeddings methods have emerged, that provide powerful algorithms to allow semantic understanding without the task of creating large amounts of annotated test data. One problem with this type of binary classification, is that the sentiment output will be in the form of '1' (positive) or '0' (negative) for the string of text in the tweet, regardless if there are one or more entities referred to in the text. This paper plans to enhance the word embeddings approach with the deployment of a sentiment lexicon-based technique to appoint a total score that indicates the polarity of opinion in relation to a particular entity or entities. This type of sentiment classification is a way of associating a given entity with the adjectives, adverbs, and verbs describing it, and extracting the associated sentiment to try and infer if the text is positive or negative in relation to the entity or entities.
Original language | English |
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Title of host publication | International Conference on Recent Advances in Natural Language Processing |
Subtitle of host publication | Meet Deep Learning, RANLP 2017 - Proceedings |
Editors | Ruslan Mitkov, Irina Temnikova, Kalina Bontcheva, Ivelina Nikolova, Galia Angelova |
Publisher | Association for Computational Linguistics |
Pages | 733-740 |
Number of pages | 8 |
Volume | 2017-September |
ISBN (Electronic) | 9789544520489 |
DOIs | |
Publication status | Published - 01 Jan 2017 |
Event | 11th International Conference on Recent Advances in Natural Language Processing, RANLP 2017 - Varna, Bulgaria Duration: 02 Sept 2017 → 08 Sept 2017 |
Conference
Conference | 11th International Conference on Recent Advances in Natural Language Processing, RANLP 2017 |
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Country/Territory | Bulgaria |
City | Varna |
Period | 02/09/2017 → 08/09/2017 |
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Artificial Intelligence
- Electrical and Electronic Engineering
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Dive into the research topics of 'Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach'. Together they form a unique fingerprint.Student theses
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Sentiment Analysis on Twitter feeds to establish opinion towards entities in single entity and multi-entity texts
Sweeney, C. (Author), Padmanabhan, D. (Supervisor) & Miller, P. (Supervisor), Jul 2019Student thesis: Masters Thesis › Master of Philosophy
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