Word Sense Disambiguation in the domain of Sentiment Analysis through Deep Learning

Sentiment analysis forms part of a major component of Natural Language Processing (NLP), even though continuous improvements in NLP are being made, word disambiguation remains a complex problem within the domain of sentiment analysis (Navigli, 2009). Word Sense Disambiguation (WSD) is a problem that deals with identifying the correct sense of ambiguous words in a sentence. As such, various words can have multiple meanings depending on the context in which they are used. Although advances in deep learning continue to rise within the NLP domain, WSD is still a task in which deep learning is yet to be fully explored. Whilst there does exist research within WSD as a whole, there is limited research for WSD conducted within the domain of sentiment analysis (Seifollahi and Shajari, 2019). The proposed research explores the task of WSD in the domain of sentiment analysis through recent advances in deep neural networks with a specific focus on 1D Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) algorithms. Sentiments expressed in text sourced from the Amazon product reviews data were analysed using 1D CNN and LSTM deep learning algorithms. The Amazon product reviews data is segmented according to the type of product category which is essentially a context category. The effectiveness of each algorithm was evaluated from a statistical performance and efficiency perspective. It was found that the inclusion of context as a model input, improves the model out of sample performance as compared to a model without context as an input. In addition to this, it was observed that including more context categories as an input had improved the out of sample performance for both 1D CNN and LSTM algorithms. Furthermore, the 1D CNN exhibited superior performance over the LSTM model from a statistical and efficiency stand-point. Given that there has not been a considerable amount of research which explores the application of deep learning to solving the problem of WSD within sentiment analysis, the findings of this research will aid in providing a base-level of knowledge on future potential exploration and applications for WSD relating to sentiment analysis.

Grantor

Department of Statistical Sciences

Year dc:date.issued

2022

Author dc:creator

• Baiju, Vedanth

Advisors dc:contributor.advisor

• Er, Sebnem
• Dufourq, Emmanuel

• Statistical Sciences

Handle dc:identifier.uri

http://hdl.handle.net/11427/36965

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/36965