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https://github.com/sanjanahombal/study-on-sentiment-analysis

This project explores the optimal combination of Bag-of-Words and TF-IDF vectorization with Naive Bayes and SVM for sentiment analysis. It evaluates performance using accuracy, precision, recall, and F1-score, addressing ethical concerns like data privacy and bias to improve sentiment classification in real-world applications.
https://github.com/sanjanahombal/study-on-sentiment-analysis

bag-of-words confusionmatrix googlecollab gridsearch-crossvalidation matplotlib-pyplot naive-bayes-classifier numpy pandas seaborn sklearn svm-classifier tfidf-vectorizer

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This project explores the optimal combination of Bag-of-Words and TF-IDF vectorization with Naive Bayes and SVM for sentiment analysis. It evaluates performance using accuracy, precision, recall, and F1-score, addressing ethical concerns like data privacy and bias to improve sentiment classification in real-world applications.

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# A Study on Sentiment Analysis

##Executive Summary:

Using Python, Naive Bayes, Support Vector Machines (SVM), and vectorization techniques like Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF), I developed a model to analyze sentiments from large-scale textual data, including social media posts and product reviews. After comparing the performance of these models, I found that SVM combined with TF-IDF provided the most accurate sentiment predictions. I recommend implementing these methods for tasks involving large, unstructured datasets, as they significantly improve the accuracy of sentiment classification.



##Business Problem:

In today’s digital landscape, understanding customer sentiments from massive amounts of unstructured text data is crucial. This is particularly important for industries like marketing, customer service, and product development. Companies need an efficient way to analyze opinions and feedback from sources such as social media, customer reviews, and surveys. The challenge lies in identifying the most accurate methods for extracting and classifying sentiments, allowing businesses to improve products and customer satisfaction.



##Methodology:

###Data Collection and Preprocessing:

-Gathered textual data from social media and customer reviews.

-Cleaned and preprocessed data by removing noise (e.g., punctuation, special characters), tokenizing, and applying lemmatization.

-Managed missing data by removing or imputing values.



###Text Vectorization:

-Implemented Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) techniques to convert text into numerical features for machine learning models.



###Machine Learning Models:

-Applied Naive Bayes and Support Vector Machine (SVM) classifiers to predict sentiment.

-Used Grid Search and Cross-Validation to fine-tune hyperparameters.



##Performance Metrics:

-Evaluated model performance using accuracy, precision, recall, and F1-score.

-Conducted a comparative analysis between BoW and TF-IDF as well as Naive Bayes and SVM.



##Skills Used:

-Python: Pandas, Numpy, Scikit-learn, Matplotlib

-Machine Learning Techniques: Naive Bayes, SVM, Grid Search, Cross-Validation

-Text Processing: Tokenization, Lemmatization, Stop Words Removal

-Text Vectorization: Bag of Words (BoW), TF-IDF



##Results & Business Recommendation:

The analysis revealed that the combination of SVM and TF-IDF provided the highest accuracy (87.3%) in classifying sentiment, outperforming Naive Bayes and BoW. This model allows companies to extract valuable insights from customer feedback with a higher degree of reliability.



##Key findings include:

-SVM with TF-IDF had the highest precision (88.2%) for detecting positive sentiments.

-Naive Bayes with BoW had lower accuracy (82.1%) but was faster to compute, making it suitable for tasks with computational constraints.

-Based on these results, I recommend using SVM with TF-IDF for tasks where accuracy is paramount, such as sentiment analysis on product reviews or social media trends. For real-time applications with large datasets, Naive Bayes with BoW can be a more computationally efficient option.



Recommendations:

-Implement SVM with TF-IDF for sentiment analysis in customer feedback systems.

-Integrate real-time monitoring of sentiment trends using these models to make data-driven decisions.

-Explore A/B testing for refining sentiment analysis algorithms.



Next Steps:

-Test advanced vectorization techniques, such as Word2Vec or transformer-based models like BERT.

-Evaluate the models on larger and more diverse datasets to ensure generalizability.

-Incorporate interpretability tools (e.g., SHAP, LIME) to understand which features most influence the model's predictions.