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Farha Kousar edited this page Nov 23, 2023 · 1 revision
  1. Question: Can you explain the main objective of your project, "Online Shopping Sentiment Analysis on Flipkart"?

Answer: The main objective of our project is to analyze customer reviews on Flipkart, extracting sentiments to understand user experiences and preferences. We aim to provide valuable insights for both customers and the platform to enhance overall satisfaction.

  1. Question: What techniques did you use for data preprocessing in your project?

Answer: We employed several data preprocessing techniques, including:

Lowercasing: Converting all text to lowercase for consistency. Removing Punctuation: Eliminating unnecessary symbols and punctuation. Stopword Removal: Filtering out common words that do not contribute much to sentiment. Stemming: Reducing words to their root form for uniformity. 3. Question: How did you approach sentiment analysis, and what machine learning algorithms were used for classification?

Answer: We used a combination of natural language processing (NLP) techniques and machine learning algorithms. The process involved:

Feature extraction from reviews. Clustering using the ROCK algorithm. Classification using the CART algorithm to label reviews as positive or negative. 4. Question: Can you explain the significance of clustering in your sentiment analysis process?

Answer: Clustering helps group similar reviews together, revealing patterns in user sentiments. It allows us to identify common themes or topics in customer feedback, providing a more nuanced understanding of product satisfaction.

  1. Question: How did you visualize the results of your sentiment analysis?

Answer: We utilized Matplotlib, Seaborn, and WordCloud for visualization. Matplotlib and Seaborn were used to create charts displaying sentiment distribution, while WordCloud was employed to visually represent frequently occurring words in the reviews.

  1. Question: What challenges did you face during the project, and how did you overcome them?

Answer: Challenges included handling large datasets efficiently and addressing imbalances in sentiment labels. We implemented techniques such as data sampling and balancing strategies to overcome these challenges and ensure the robustness of our sentiment analysis.

  1. Question: What is the objective of your Online Shopping Sentiment Analysis project on Flipkart?

Answer: The objective of the project is to analyze customer reviews on Flipkart, extract sentiments expressed in these reviews, and provide insights into user experiences and preferences on the e-commerce platform.

  1. Question: How did you collect data for sentiment analysis on Flipkart?

Answer: We utilized the Flipkart product API to extract reviews, ratings, and brand information. The API provided a structured way to gather relevant data on customer sentiments from user comments on the platform.

  1. Question: Explain the data preprocessing steps you implemented in your project.

Answer: Data preprocessing involved cleaning the extracted data by removing noise and irrelevant information. Text preprocessing included lowercasing, removing punctuation, and stemming to prepare the text data for analysis.

  1. Question: Which machine learning algorithms did you use for clustering similar reviews?

Answer: We employed the ROCK clustering algorithm to group similar reviews based on content and sentiment. This helped identify patterns and themes within the customer feedback.

  1. Question: Describe the classification algorithm used for labeling reviews as positive or negative.

Answer: We implemented the CART (Classification and Regression Trees) algorithm for sentiment classification. The model was trained on labeled data to predict whether a review expresses a positive or negative sentiment.

  1. Question: How did you visualize the sentiment analysis results?

Answer: Matplotlib, Seaborn, and WordCloud were used for visualization. We created charts to represent sentiment distribution and word clouds to highlight frequently occurring words in reviews.

  1. Question: What insights did you derive from the sentiment analysis, and how can they be beneficial for businesses?

Answer: The analysis provided insights into product popularity, customer satisfaction, and prevalent sentiments. These insights can guide businesses in making data-driven decisions, improving products, and enhancing the overall customer experience.

  1. Question: Explain the role of Jupyter Notebook in your project.

Answer: Jupyter Notebook served as the primary development environment for coding and analysis. It provided an interactive and user-friendly interface for running Python code and documenting the analysis process

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