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Sentiment-Analysis
# Sentiment-analysis - Sentimental analysis on Movie review is explained where the reviews are classified into Positive , Negative and Neutral. - The task is to learn to predict movie ratings based on the review.  # DATA COLLECTION - Data used for this project is from Kaggle (https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews/data) - Data has training and test zip folders. - It is a large data set with 156060 reviews in training set and 66292 reviews in test set. - Dataset has two fields: - id - an id unique to a given file - Sentiment labels range between 0 – 4. The sentiment labels are: - 0 - negative - 1 - somewhat negative - 2 - neutral - 3 - somewhat positive - 4 - positive # PROCESSING - Data cleaning is performed such as removing null values and blank lines. - Processing function is defined where stemming, lemmatizing and stopwords are removed to reduce the dimensionality of the data and improve the model performance. - Stemming – suffix of words are removed. - Lemmatizing – similar to stemming but retains the dictionary form of the word - Stopwords – removing the common words such as 'is', 'the' etc. - Also, the target variable ‘Sentiment’ was converted to three unique values from 5, where , - 0 - negative - 1 - neutral - 2 - positive # VISUALIZATION  - The pie chart shows the distribution of the sentiments 0, 1 and 2 . - We can infer that - Negative sentiment(0) has 22% of the whole reviews. - Neutral (1)contributes to half the reviews of about 50.99%. - Positive (2) has 27% reviews. # Result - Classification Report - An overall of 90% on Training data and 70% on test set is achieved. - The precision , recall and f1 score ranges between 65% to 75 % indicating a model to be pretty good  # TABLEAU - Tableau is used for visualization to get insights about the data. - It has an advantage of handling large data and is super interactive.  # Refrences 1) https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews/data 2) https://prod-ca-a.online.tableau.com/#/site/georgianc/views/Assign_AI_Infra/Sheet63?:iid=2 3) https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html 4) https://www.nltk.org/
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README preview
Sentiment-analysis
- Sentimental analysis on Movie review is explained where the reviews are classified into Positive , Negative and Neutral.
- The task is to learn to predict movie ratings based on the review.
DATA COLLECTION
- Data used for this project is from Kaggle (https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews/data)
- Data has training and test zip folders.
- It is a large data set with 156060 reviews in training set and 66292 reviews in test set.
- Dataset has two fields:
- id - an id unique to a given file
- Sentiment labels range between 0 – 4. The sentiment labels are:
- 0 - negative
- 1 - somewhat negative
- 2 - neutral
- 3 - somewhat positive
- 4 - positive
PROCESSING
- Data cleaning is performed such as removing null values and blank lines.
- Processing function is defined where stemming, lemmatizing and stopwords are removed to reduce the dimensionality of the data and improve the model performance.
- Stemming – suffix of words are removed.
- Lemmatizing – similar to stemming but retains the dictionary form of the word
- Stopwords – removing the common words such as 'is', 'the' etc.
- Also, the target variable ‘Sentiment’ was converted to three unique values from 5, where ,
- 0 - negative
- 1 - neutral
- 2 - positive
VISUALIZATION
- The pie chart shows the distribution of the sentiments 0, 1 and 2 .
- We can infer that
- Negative sentiment(0) has 22% of the whole reviews.
