{"id":249,"date":"2025-11-20T15:08:05","date_gmt":"2025-11-20T15:08:05","guid":{"rendered":"https:\/\/codetypingpro.com\/?p=249"},"modified":"2025-11-20T15:08:05","modified_gmt":"2025-11-20T15:08:05","slug":"38-real-world-python-projects-sentiment-analysis-dashboard","status":"publish","type":"post","link":"https:\/\/codetypingpro.com\/?p=249","title":{"rendered":"38 – Real-World Python Projects – Sentiment Analysis Dashboard"},"content":{"rendered":"\n
<\/h6>\n\n\n\n

Goal<\/strong><\/h3>\n\n\n\n

Create an interactive dashboard that analyzes text sentiment (positive\/negative\/neutral) from:<\/p>\n\n\n\n

\u2714 User input
\u2714 Tweets
\u2714 Reviews
\u2714 Uploaded CSV files<\/p>\n\n\n\n

Displays visual charts (pie chart, bar chart, timeline).<\/p>\n\n\n\n

\n\n\n\n

\ud83e\udde0 What the Project Will Do<\/strong><\/h1>\n\n\n\n

The dashboard can:<\/h3>\n\n\n\n

\u2714 Accept text input
\u2714 Analyze sentiment in real-time
\u2714 Upload a CSV of comments
\u2714 Process each comment using NLP
\u2714 Show counts of Positive, Neutral, Negative
\u2714 Show sentiment distribution graph
\u2714 Show average sentiment score
\u2714 Show word cloud (optional)<\/p>\n\n\n\n

\n\n\n\n

\ud83e\uddf0 Tech Stack<\/strong><\/h1>\n\n\n\n
    \n
  • Python<\/strong><\/li>\n\n\n\n
  • TextBlob \/ NLTK \/ Transformers<\/strong><\/li>\n\n\n\n
  • Streamlit<\/strong> (dashboard)<\/li>\n\n\n\n
  • Matplotlib \/ Plotly<\/strong><\/li>\n\n\n\n
  • Pandas<\/strong><\/li>\n<\/ul>\n\n\n\n
    \n\n\n\n

    \ud83d\udcc1 Folder Structure<\/h1>\n\n\n\n
    SentimentDashboard\/\n\u2502\u2500\u2500 app.py\n\u2502\u2500\u2500 requirements.txt\n\u2502\u2500\u2500 sample_reviews.csv\n<\/code><\/pre>\n\n\n\n
    \n\n\n\n
    \ud83d\udce6 requirements.txt<\/h1>\n\n\n\n
    streamlit\npandas\ntextblob\nmatplotlib\nwordcloud\n<\/code><\/pre>\n\n\n\n
    Install all:<\/p>\n\n\n\n
    pip install -r requirements.txt\n<\/code><\/pre>\n\n\n\n
    or manually:<\/p>\n\n\n\n
    pip install streamlit textblob pandas matplotlib wordcloud\npython -m textblob.download_corpora\n<\/code><\/pre>\n\n\n\n
    \n\n\n\n
    \ud83e\udde9 Full Working Sentiment Dashboard Code (app.py)<\/strong><\/h1>\n\n\n\n
    import streamlit as st\nimport pandas as pd\nfrom textblob import TextBlob\nimport matplotlib.pyplot as plt\n\nst.title(\"Sentiment Analysis Dashboard\")\nst.write(\"Analyze text sentiment in real-time\")\n\ndef analyze_sentiment(text):\n    score = TextBlob(text).sentiment.polarity\n    if score > 0:\n        return \"Positive\", score\n    elif score < 0:\n        return \"Negative\", score\n    else:\n        return \"Neutral\", score\n\n# --- Option 1: Single Text Input ---\nst.header(\"Single Text Sentiment\")\nuser_text = st.text_area(\"Enter text\")\n\nif st.button(\"Analyze\"):\n    sentiment, score = analyze_sentiment(user_text)\n    st.success(f\"Sentiment: {sentiment} (Score: {score})\")\n\n# --- Option 2: CSV Upload ---\nst.header(\"CSV Sentiment Analysis\")\nfile = st.file_uploader(\"Upload CSV with 'text' column\", type=['csv'])\n\nif file is not None:\n    df = pd.read_csv(file)\n    df[\"Sentiment\"] = df[\"text\"].apply(lambda x: analyze_sentiment(x)[0])\n    df[\"Score\"] = df[\"text\"].apply(lambda x: analyze_sentiment(x)[1])\n    st.write(df)\n\n    # Count sentiment values\n    counts = df[\"Sentiment\"].value_counts()\n\n    # Plot\n    st.subheader(\"Sentiment Distribution\")\n    fig, ax = plt.subplots()\n    ax.pie(counts, labels=counts.index, autopct=\"%1.1f%%\")\n    st.pyplot(fig)\n<\/code><\/pre>\n\n\n\n
    \n\n\n\n
    \u25b6 Run the Dashboard<\/h1>\n\n\n\n
    streamlit run app.py\n<\/code><\/pre>\n\n\n\n
    Opens in browser:<\/p>\n\n\n\n
    http://localhost:8501\n<\/code><\/pre>\n\n\n\n
    \n\n\n\n
    \ud83d\udcca Features in Dashboard<\/strong><\/h1>\n\n\n\n
    1. Sentiment Analyzer<\/strong><\/h3>\n\n\n\n
      \n
    • Positive<\/li>\n\n\n\n
    • Negative<\/li>\n\n\n\n
    • Neutral<\/li>\n\n\n\n
    • Shows sentiment score (-1 to +1)<\/li>\n<\/ul>\n\n\n\n
      2. CSV Analysis<\/strong><\/h3>\n\n\n\n
      Upload file like:<\/p>\n\n\n\n
      text\n\"Great product\"\n\"Very bad service\"\n\"Okay, not good not bad\"\n<\/code><\/pre>\n\n\n\n
      Dashboard shows:<\/p>\n\n\n\n
        \n
      • Table with sentiment<\/li>\n\n\n\n
      • Pie chart<\/li>\n\n\n\n
      • Score distribution<\/li>\n<\/ul>\n\n\n\n
        3. Graphs<\/strong><\/h3>\n\n\n\n
        Optional additions:<\/p>\n\n\n\n
        \u2b50 Bar chart
        \u2b50 Word cloud
        \u2b50 Sentiment over time<\/p>\n\n\n\n
        <\/p>\n","protected":false},"excerpt":{"rendered":"
        Goal Create an interactive dashboard that analyzes text sentiment (positive\/negative\/neutral) from: \u2714 User input\u2714 Tweets\u2714 Reviews\u2714 Uploaded CSV files Displays visual charts (pie chart, bar chart, timeline). \ud83e\udde0 What the Project Will Do The dashboard can: \u2714 Accept text input\u2714 Analyze sentiment in real-time\u2714 Upload a CSV of comments\u2714 Process each comment using NLP\u2714 Show […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-249","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/posts\/249","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=249"}],"version-history":[{"count":1,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/posts\/249\/revisions"}],"predecessor-version":[{"id":250,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=\/wp\/v2\/posts\/249\/revisions\/250"}],"wp:attachment":[{"href":"https:\/\/codetypingpro.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=249"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=249"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/codetypingpro.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=249"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}