nltk movie reviews dataset

Sentiment Analysis on Movie Reviews: A Comparative Analysis

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Sentiment Analysis on Movie Review Using Deep Learning RNN Method

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• First Online: 30 August 2020
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• Priya Patel 18 ,
• Devkishan Patel 19 &
• Chandani Naik 20  

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1177))

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The usage of social media grows rapidly because of the functionality like easy to use and it will also allow user to connect with all around the globe to share the ideas. It is desired to automatically use the information which is user’s interest. One of the meaningful information that is derived from the social media sites are sentiments. Sentiment analysis is used for finding relevant documents, overall sentiment, and relevant sections; quantifying the sentiment; and aggregating all sentiments to form an overview. Sentiment analysis for movie review classification is useful to analyze the information in the form of number of reviews where opinions are either positive or negative. In this paper we had applied the deep learning-based classification algorithm RNN, measured the performance of the classifier based on the pre-process of data, and obtained 94.61% accuracy. Here we had used RNN algorithm instead of machine learning algorithm because machine learning algorithm works only in single layer while RNN algorithm works on multilayer that gives you better output as compared to machine learning.

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Department of Computer Engineering, N. G. Polytechnic, Isroli, India

Priya Patel

Department of Computer Engineering, Pacific School of Engineering, Palsana, India

Devkishan Patel

Department of Computer Engineering, CGPIT, Uka Tarsadiya University, Bardoli, India

Chandani Naik

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Correspondence to Priya Patel .

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School of Computer Engineering, Kalinga Institute Industrial Technology, Bhubaneswar, Odisha, India

Suresh Chandra Satapathy

Department of Informatics, University of Leicester, Leicester, UK

Yu-Dong Zhang

Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

Vikrant Bhateja

School of Management, National Institute of Technology Karnataka, Surathkal, Karnataka, India

Ritanjali Majhi

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Patel, P., Patel, D., Naik, C. (2021). Sentiment Analysis on Movie Review Using Deep Learning RNN Method. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_15

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DOI : https://doi.org/10.1007/978-981-15-5679-1_15

Published : 30 August 2020

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Sentiment Classification on the Large Movie Review Dataset

Data mining project, bert sentiment classification.

• Monticone Pietro
• Moroni Claudio
• Orsenigo Davide

Problem: Sentiment Classification

A sentiment classification problem consists, roughly speaking, in detecting a piece of text and predicting if the author likes or dislikes what he/she is talking about: the input X is a piece of text and the output Y is the sentiment we want to predict, such as the rating of a movie review.

If we can train a model to map X to Y based on a labelled dataset then it can be used to predict sentiment of a reviewer after watching a movie.

Data: Large Movie Review Dataset v1.0

The dataset contains movie reviews along with their associated binary sentiment polarity labels.

• The core dataset contains 50,000 reviews split evenly into 25k train and 25k test sets.
• The overall distribution of labels is balanced (25k pos and 25k neg).
• 50,000 unlabeled documents for unsupervised learning are included, but they won’t be used.
• The train and test sets contain a disjoint set of movies, so no significant performance is obtained by memorizing movie-unique terms and their associated with observed labels.
• In the labeled train/test sets, a negative review has a score ≤ 4 out of 10, and a positive review has a score ≥ 7 out of 10. Thus reviews with more neutral ratings are not included in the train/test sets.
• In the unsupervised set, reviews of any rating are included and there are an even number of reviews > 5 and ≤ 5.

Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis . The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011).

Theoretical introduction

The encoder-decoder sequence.

Roughly speaking, an encoder-decoder sequence is an ordered collection of steps ( coders ) designed to automatically translate sentences from a language to another (e.g. the English “the pen is on the table” into the Italian “la penna è sul tavolo”), which could be useful to visualize as follows: input sentence → ( encoders ) → ( decoders ) → output/translated sentence .

For our practical purpose, encoders and decoders are effectively indistinguishable (that’s why we will call them coders ): both are composed of two layers: a LSTM or GRU neural network and an attention module (AM) . They only differ in the way in which their output is processed.

LSTM or GRU neural network

Both the input and the output of an LSTM/GRU neural network consists of two vectors:

• the hidden state : the representation of what the network has learnt about the sentence it’s reading;
• the prediction : the representation of what the network predicts (e.g. translation).

Each word in the English input sentence is translated into its word embedding vector (WEV) before being processed by the first coder (e.g. with word2vec ). The WEV of the first word of the sentence and a random hidden state are processed by the first coder of the sequence. Regarding the output: the prediction is ignored, while the hidden state and the WEV of the second word are passed as input into the second coder and so on to the last word of the sentence. Therefore in this phase the coders work as encoders .

At the end of the sequence of N encoders (N being the number of words in the input sentence), the decoding phase begins:

• the last hidden state and the WEV of the “START” token are passed to the first decoder ;
• the decoder outputs a hidden state and a prection;
• the hidden state and the prediction are passed to the second decoder;
• the second decoder outputs a new hidden state and the second word of the translated/output sentence

and so on up until the whole sentence has been translated, namely when a decoder of the sequence outputs the WEV of the “END” token. Then there is an external mechanism to convert prediction vectors into real words, so it’s very importance to notice that the only purpose of decoders is to predict the next word .

Attention module (AM)

The attention module is a further layer that is placed before the network which provides the collection of words of the sentence with a relational structure. Let’s consider the word “table” in the sentence used as an exampe above. Because of the AM, the encoder will weight the preposition “on” (processed by the previous encoder) more than the article “the” which refers to the subject “cat”.

Bidirectional Encoder Representations from Transformers (BERT)

Transformer.

The transformer is a coder endowed with the AM layer. Transformers have been observed to work much better than the basic encoder-decoder sequences.

BERT is a sequence of encoder-type transformers which was pre-trained to predict a word or sentence (i.e. used as decoder). The benefit of improved performance of Transformers comes at a cost: the loss of bidirectionality , which is the ability to predict both next word and the previous one. BERT is the solution to this problem, a Tranformer which preserves biderectionality .

The first token is not “START”. In order to use BERT as a pre-trained language model for sentence-classification, we need to input the BERT prediction of “CLS” into a linear regression because

• the model has been trained to predict the next sentence, not just the next word;
• the semantic information of the sentence is encoded in the prediction output of “CLS” as a document vector of 512 elements.

• bert_final_data
• https://www.kaggle.com/dataset/5f1193b4685a6e3aa8b72fa3fdc427d18c3568c66734d60cf8f79f2607551a38
• https://www.kaggle.com/dataset/9850d2e4b7d095e2b723457263fbef547437b159e3eb7ed6dc2e88c7869fca0b
• Bert-For-Tf2
• Google github repository
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• A Visual Guide to Using BERT for the First Time
• Machine Translation(Encoder-Decoder Model)!
• The Illustarted Tranformers
• The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)
• BERT Explained: State of the art language model for NLP
• Learning Word Vectors for Sentiment Analysis .

Documentation

NLTK Documentation

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• Open Issues
• NLTK on GitHub

Installation

• Installing NLTK
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• Release Notes
• Contributing to NLTK

Source code for nltk.corpus.reader.reviews

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Objective 2.1) “This report aims to use the NLP techniques including chunking, NER, tokenization, and stopwords removal to have the sentiment analysis on the IMDb dataset.” (KEYWORDS USED: IMDb, NLP, Chunking, NER, Tokenization, Stopwords removal, Sentiment analysis, Dataset)

2.2) “The primary objective of the report is to showcase the impact of comprehensive preprocessing towards improving the accuracy and effectiveness of sentiment classification models made for analyzing movie reviews. Added to the preprocessing, the report also includes EDA, Basic NLTK, Vader Sentimental Analysis” (KEYWORDS USED: Preprocessing, Accuracy, Effectiveness, Sentiment classification models, Movie reviews, EDA, Basic NLTK, Vader Sentiment Analysis)

2.3) “The report also seeks to provide insights highlighting the importance of cleaning the dataset and preprocessing, while utilizing the machine learning algorithms in the realm of the NLP and refining the feature sets for sentiment analysis.”

(KEYWORDS USED: Cleaning, Preprocessing, Machine learning algorithms, NLP, Feature sets, Sentiment analysis)

2.4) “I submit this report, in hopes of using this preprocessed dataset for further projects, if not research pertaining with implications for broader applications in understanding and interpreting textual sentiments.”

(KEYWORDS USED: Report submission, Preprocessed dataset, Further projects, Research, Implications, Broader applications, Understanding, Interpreting, Textual sentiments)

About the dataset

The dataset utilized in the project is on the infamous Internet Movie Database (IMDb), an online repository of information related to various sitcoms, movies, series, and even video games. According to several critics and customers of their own, IMDb is said to be the authoritative and the largest internet platform that supports several user-generated content, and reviews from critics and also celebrates an inclusive and diverse perspective on a vast array of public and media.

The dataset extracted that is used in the project, is sourced from Kaggle and consists of quite a substantial movie and series reviews, accompanied by a label indicating whether or not the reviews by the user are positive or negative. The purpose of my choosing this dataset despite the volume of rows in the same is due to its contribution to providing a varied and equal collection of textual data and to understand how the data can be trained and the machine learning models can be evaluated over the same. The dataset is a clear reflection of real-world movie sentiments, however, the names of the movies are not included in the dataset, capturing the nuances of an individual’s choice of words to communicate their feelings and viewpoints. It is a given that IMDb’s prominence and diversity of its user-contributed reviews make the dataset one of a kind and particularly advantageous in the field of sentiment analysis

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