Customer-Sentiment-Analysis

• Sentiment analysis (or opinion mining) is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral.
• Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.
• Executed NLP techniques to analyze the transcripts and determine the sentiment expressed by customers towards different companies. Performed EDA on the dataset to identify key insights and patterns in customer sentiment.
• Classified the sentiment as positive or negative. Implemented Naive Bayes, Decision Tree and KNN classification models with 79% of accuracy. Created Visualizations and dashboard in tableau.

Types of Sentiment Analysis

• Graded Sentiment Analysis

• Emotion detection

• Aspect-based Sentiment Analysis

Problem Statement

Customer sentiment analysis based on voice transcript, chat transcript and email transcript. Identify the customer sentiment against different companies. Analysis the dataset and identify key insights.

Project Objective

• Utilize natural language processing techniques to analyze the transcripts and determine the sentiment expressed by customers towards different companies.
• Classify the sentiment as positive, negative, or neutral.
• Develop a machine learning or deep learning model to automatically classify the customer sentiment based on the transcripts.
• Visualize the analysis results using charts, graphs, and visual representations to effectively communicate the findings.

Methodology

NLP is used to detect positive or negative sentiment in text. Used python for Data cleaning and Data preprocessing of the dataset. Created Visualizations and dashboard in tableau. Implemented Naive Bayes, Decision Tree and KNN classification models with 79% of accuracy.

• Data Collection: Gather voice transcripts, chat transcripts, and email transcripts from customers across different companies, covering a wide range of interactions and feedback.

• Customer Sentiment Analysis: Utilize natural language processing techniques to analyze the transcripts and determine the sentiment expressed by customers towards different companies. Classify the sentiment as positive, negative, or neutral.

• Sentiment Classification Model: Develop a machine learning or deep learning model to automatically classify the customer sentiment based on the transcripts. Train the model using labeled data and fine-tune it to improve accuracy.

• Key Insights Extraction: Perform exploratory data analysis on the dataset to identify key insights and patterns in customer sentiment. Extract important features and metrics such as frequently mentioned topics, sentiment distribution across companies, and sentiment trends over time.

• Visualization and Reporting: Visualize the analysis results using charts, graphs, and visual representations to effectively communicate the findings. Generate comprehensive reports summarizing the sentiment analysis and key insights for each company.

• Sentiment Comparison: Compare the customer sentiment across different companies to identify potential areas of improvement or strengths for each organization. Highlight the differences in sentiment between companies and provide recommendations for enhancing customer satisfaction.

• Actionable Recommendations: Provide actionable recommendations to companies based on the sentiment analysis and key insights. Suggest strategies for improving customer experience, addressing specific pain points, and strengthening customer relationships.