Comparison of VADER and LSTM for sentiment analysis

R. Adarsh, Ashwin Patil, Shubham Rayar, K. M. Veena

Research output: Contribution to journalArticle

Abstract

Sentiment analysis is one of the trending topics at present. It has a vast scope from analysing the mood of the person based on his tweet, to predicting the stock prices. But this field is quite challenging. It is not easy to make a machine understand what exactly the person is saying. In this paper, we are going to demonstrate two different methods that can be used in sentiment analysis and its comparison. The two methods used in this paper are: i) VADER-Valence Aware Dictionary for sEntiment Reasoning ii) LSTM model (Long Short-Term Memory). VADER uses a lexicon-based approach, where the lexicon contains the intensity of all the sentiment showing words. The intensities are fetched, the sentiment score is calculated and based on this sentiment score, the review is classified as either positive or negative. We used VADER from NLTK module of python for our study. Recurrent Neural Network has proved its results in a variety of problems like speech recognition, language modelling, and translation. We used LSTM which is an extension of RNN for our study. LSTM networks are very effective for sequential data like texts because they can relate the context of the sentence very well. We preferred LSTM over RNN as LSTM supports Long-term dependency which will help us predict our reviews better. We implemented the LSTM model using keras.

Original languageEnglish
Pages (from-to)540-543
Number of pages4
JournalInternational Journal of Recent Technology and Engineering
Volume7
Issue number6
Publication statusPublished - 01-03-2019
Externally publishedYes

Fingerprint

Recurrent neural networks
Glossaries
Speech recognition
Sentiment analysis
Sentiment
Long short-term memory
Language modeling
Valence
Mood
Stock prices
Module

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Management of Technology and Innovation

Cite this

Adarsh, R., Patil, A., Rayar, S., & Veena, K. M. (2019). Comparison of VADER and LSTM for sentiment analysis. International Journal of Recent Technology and Engineering, 7(6), 540-543.
Adarsh, R. ; Patil, Ashwin ; Rayar, Shubham ; Veena, K. M. / Comparison of VADER and LSTM for sentiment analysis. In: International Journal of Recent Technology and Engineering. 2019 ; Vol. 7, No. 6. pp. 540-543.
@article{bda431386a674e66a5cf5671c7144d8d,
title = "Comparison of VADER and LSTM for sentiment analysis",
abstract = "Sentiment analysis is one of the trending topics at present. It has a vast scope from analysing the mood of the person based on his tweet, to predicting the stock prices. But this field is quite challenging. It is not easy to make a machine understand what exactly the person is saying. In this paper, we are going to demonstrate two different methods that can be used in sentiment analysis and its comparison. The two methods used in this paper are: i) VADER-Valence Aware Dictionary for sEntiment Reasoning ii) LSTM model (Long Short-Term Memory). VADER uses a lexicon-based approach, where the lexicon contains the intensity of all the sentiment showing words. The intensities are fetched, the sentiment score is calculated and based on this sentiment score, the review is classified as either positive or negative. We used VADER from NLTK module of python for our study. Recurrent Neural Network has proved its results in a variety of problems like speech recognition, language modelling, and translation. We used LSTM which is an extension of RNN for our study. LSTM networks are very effective for sequential data like texts because they can relate the context of the sentence very well. We preferred LSTM over RNN as LSTM supports Long-term dependency which will help us predict our reviews better. We implemented the LSTM model using keras.",
author = "R. Adarsh and Ashwin Patil and Shubham Rayar and Veena, {K. M.}",
year = "2019",
month = "3",
day = "1",
language = "English",
volume = "7",
pages = "540--543",
journal = "International Journal of Recent Technology and Engineering",
issn = "2277-3878",
publisher = "Blue Eyes Intelligence Engineering and Sciences Publication",
number = "6",

}

Adarsh, R, Patil, A, Rayar, S & Veena, KM 2019, 'Comparison of VADER and LSTM for sentiment analysis', International Journal of Recent Technology and Engineering, vol. 7, no. 6, pp. 540-543.

Comparison of VADER and LSTM for sentiment analysis. / Adarsh, R.; Patil, Ashwin; Rayar, Shubham; Veena, K. M.

In: International Journal of Recent Technology and Engineering, Vol. 7, No. 6, 01.03.2019, p. 540-543.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Comparison of VADER and LSTM for sentiment analysis

AU - Adarsh, R.

AU - Patil, Ashwin

AU - Rayar, Shubham

AU - Veena, K. M.

PY - 2019/3/1

Y1 - 2019/3/1

N2 - Sentiment analysis is one of the trending topics at present. It has a vast scope from analysing the mood of the person based on his tweet, to predicting the stock prices. But this field is quite challenging. It is not easy to make a machine understand what exactly the person is saying. In this paper, we are going to demonstrate two different methods that can be used in sentiment analysis and its comparison. The two methods used in this paper are: i) VADER-Valence Aware Dictionary for sEntiment Reasoning ii) LSTM model (Long Short-Term Memory). VADER uses a lexicon-based approach, where the lexicon contains the intensity of all the sentiment showing words. The intensities are fetched, the sentiment score is calculated and based on this sentiment score, the review is classified as either positive or negative. We used VADER from NLTK module of python for our study. Recurrent Neural Network has proved its results in a variety of problems like speech recognition, language modelling, and translation. We used LSTM which is an extension of RNN for our study. LSTM networks are very effective for sequential data like texts because they can relate the context of the sentence very well. We preferred LSTM over RNN as LSTM supports Long-term dependency which will help us predict our reviews better. We implemented the LSTM model using keras.

AB - Sentiment analysis is one of the trending topics at present. It has a vast scope from analysing the mood of the person based on his tweet, to predicting the stock prices. But this field is quite challenging. It is not easy to make a machine understand what exactly the person is saying. In this paper, we are going to demonstrate two different methods that can be used in sentiment analysis and its comparison. The two methods used in this paper are: i) VADER-Valence Aware Dictionary for sEntiment Reasoning ii) LSTM model (Long Short-Term Memory). VADER uses a lexicon-based approach, where the lexicon contains the intensity of all the sentiment showing words. The intensities are fetched, the sentiment score is calculated and based on this sentiment score, the review is classified as either positive or negative. We used VADER from NLTK module of python for our study. Recurrent Neural Network has proved its results in a variety of problems like speech recognition, language modelling, and translation. We used LSTM which is an extension of RNN for our study. LSTM networks are very effective for sequential data like texts because they can relate the context of the sentence very well. We preferred LSTM over RNN as LSTM supports Long-term dependency which will help us predict our reviews better. We implemented the LSTM model using keras.

UR - http://www.scopus.com/inward/record.url?scp=85065166693&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85065166693&partnerID=8YFLogxK

M3 - Article

VL - 7

SP - 540

EP - 543

JO - International Journal of Recent Technology and Engineering

JF - International Journal of Recent Technology and Engineering

SN - 2277-3878

IS - 6

ER -