SENTIMENT ANALYSIS

(NLP and Text Mining)

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Problem Statement

The main objective in this Internship Project is to predict the sentiment for a number of movie reviews obtained from the Internet Movie Database (IMDb). This dataset contains 50,000 movie reviews that have been pre-labeled with “positive” and “negative” sentiment class labels based on the review content. Besides this, there are additional movie reviews that are unlabeled.

The dataset can be obtained from http://ai.stanford.edu/~amaas/data/sentiment/ , courtesy of Stanford University and Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. They have datasets in the form of raw text as well as already processed bag of words formats. We will only be using the raw labeled movie reviews for our analyses.

Hence our task will be to predict the sentiment of 15,000 labeled movie reviews and use the remaining 35,000 reviews for training our supervised models.

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Sentiment analysis is also popularly known as opinion analysis or opinion mining. The key idea is to use techniques from text analytics, NLP, Machine Learning, and linguistics to extract important information or data points from unstructured text. This in turn can help us derive qualitative outputs like the overall sentiment being on a positive, neutral, or negative scale and quantitative outputs like the sentiment polarity, subjectivity, and objectivity proportions.

In this Coding Internship project by Suven Consultants and Technology Pvt. Ltd. , we focus on trying to analyze a large corpus of movie reviews and derive the sentiment.

We would cover a two varieties of techniques for analyzing sentiment, which include the following.

• Traditional supervised Machine Learning models
• Unsupervised lexicon-based models

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Supervised Learning

1. Setting up Dependencies

      2. Text Normalisation(using Text_normalizer.py) & Feature Engineering

A text corpus consists of multiple text documents and each document can be as simple as a single sentence to a complete document with multiple paragraphs. Textual data, in spite of being highly unstructured, can be classified into two major types of documents. Factual documents that typically depict some form of statements or facts with no specific feelings or emotion attached to them. These are also known as objective documents. Subjective documents on the other hand have text that expresses feelings, moods, emotions, and opinions.

3. Model Training , Prediction and evaluation using Model_evaluation_util.py

Here is the Jupyter Notebook of Supervised Learning

Link to Jupyter Notebook

4. Summary :-

The F1-score of the model using traditional Supervised Learning is 89.68% and an accuracy of 89.69% approximately .

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Unsupervised Lexicon Model

Unsupervised Lexicon Model :-

There are several popular lexicon models used for sentiment analysis. We would be using 3 lexicon Models mentioned below :-

· AFINN Lexicon

· SentiWordNet Lexicon

· VADER Lexicon

1. Setting up Dependencies
2. Sentiment Analysis using AFINN

• Model training,Prediction and Evaluation

Sentiment polarity is typically a numeric score that’s assigned to both the positive and negative aspects of a text document based on subjective parameters like specific words and phrases expressing feelings and emotion. Neutral sentiment typically has 0 polarity since it does not express and specific sentiment, positive sentiment will have polarity > 0, and negative < 0. Of course, you can always change these thresholds based on the type of text you are dealing with; there are no hard constraints on this.

    3. Sentiment Analysis using SentiWordNet

    4.  Sentiment Analysis using VADER

Here is the Jupyter Notebook of Unsupervised Lexicon Model

Link to Jupyter Notebook

    5.   SUMMARY :-

 

Method	F1 Score	Accuracy
AFINN	70.6	72.8
SentiWordNet	68.3	68.7
VADER	70.6	72.4

 

 

Therefore the best Unsupervised Lexicon Model is AFINN.

CONCLUSION 

On comparing the overall F1-Score and model accuracy of Supervised ML Model with the best Unsupervised Lexicon Model ,we conclude that Supervised Leaning gives us an more accurate and good model than Unsupervised Lexicon Model .