Sentiment-Analysis-on-Online-Product-Reviews
The objective of this project is to classify whether upcoming product will have positive or negative Sentiment.
Sentiment Analysis:
- Sentiment analysis and Opinion mining is the computational study of User opinion to analyze the social, psychological, philosophical, behavior and perception of an individual person or a group of people about a product, policy, services and specific situations using Machine learning technique. Sentiment analysis is an important research area that identifies the people’s sentiment underlying a text and helps in decision making about the product.
Customer Product reviews:
- A customer review is a review of a product or service made by a customer who has purchased and used, or had experience with the product or service. Customer reviews are a form of customer feedbacks on electronic commerce and online shopping sites. 90% of consumers read online reviews before purchasing product and 88% of consumers trust online reviews as much as personal recommendations.
Objective:
End-user:
E-Commerce OrganizationDataset:
The Online Product Reviews dataset includes following features: Dependent feature: reviews.rating (Unhappy,OK,Happy) Independent features: review.text (Reviews) The dataset contains 71044 rows and 25 columnsWorkflow:
Steps:
1. Data loading.
2. Data Preprocessing
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Remove Punctuations,special symbols and special characters.
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Stopword Removal
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Tokenization
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Stemming
WordCloud:
Feature Extraction:
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In order to make sense to our machine learning algorithm we have converted each review to a numeric representation which is called 'Vectorization'.
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The system uses TF-IDF Vectorizer (Term Frequency-Inverse document frequency) that transforms a count matrix to a normalized frequency representation in float.
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Splitting the data into Train and Test set (70-30).
1. TF-IDF
1.Count how many times does a word occur in each message (Known as term frequency) 2.Weigh the counts, so that frequent tokens get lower weight (inverse document frequency)
TF(t) = (Number of times term t appears in a document) / (Total number of terms in the document).
IDF(t) = log_e(Total number of documents / Number of documents with term t in it).
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Example: Consider a document containing 100 words wherein the word dog appears 3 times. The term frequency for dog is (3 / 100) = 0.03. Assume total of 10 million documents and the word dog appears in one thousand of these. Then, the inverse document frequency is calculated as log(10,000,000 / 1,000) = 4.
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Thus, the Tf-idf weight is the product of these quantities: 0.03 * 4 = 0.12.
Model Building:
Building Support Vector Machine (SVM) and SGDClassifier on feature vectors.
Results:
Thank You!