Sentiment Analysis

Sentiment Analysis of Customer Reviews

Business Task: Understanding customer sentiment is crucial for businesses to improve products and services. I developed a sentiment analysis model to classify customer reviews as positive, negative, or neutral.

Approach:

• Data Collection: Gathered customer reviews from review websites and customer feedback forms.

• Data Preprocessing: Cleaned and tokenized the text data, removed stopwords, and performed lemmatization or stemming.

• Exploratory Data Analysis (EDA): Analyzed the distribution of sentiments in the dataset and visualized word frequency distributions.

• Feature Engineering: Extracted features such as word counts, n-grams, and sentiment lexicon scores.

• Model Selection: Experimented with different classification algorithms including Naive Bayes, Support Vector Machines (SVM), and Neural Networks.

• Model Training and Evaluation: Trained the model using a labeled dataset and evaluated its performance using metrics such as accuracy, precision, recall, and F1-score.

Key Findings:

• Achieved an accuracy of 89% on the test dataset, outperforming baseline models.

• Identified keywords and phrases driving positive and negative sentiments among customers.

• Developed actionable insights for improving customer satisfaction and addressing pain points.

Skills Utilized:

• Programming Languages: Python (NLTK, Scikit-learn, TensorFlow/Keras, Matplotlib, Seaborn, CNN, Word2Vec)

• Tools: Jupyter Notebook, PyCharm

• Natural Language Processing (NLP) Techniques: Tokenization, Stopword Removal, Lemmatization, Sentiment Analysis

Results: This sentiment analysis model provides valuable insights into customer sentiment, enabling businesses to make data-driven decisions and enhance customer experiences.

# GitHub Repository: Link to GitHub Repo - On Request