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Narendra Modi - Text Speech Analysis
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## DATASET SOURCE: | ||
- https://www.kaggle.com/adiamaan/modi-speeches | ||
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## Dataset Description : | ||
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- ID | ||
- TITLE | ||
- URL | ||
- ARTICLE TEXT | ||
- IMAGES | ||
- PUBLISH INFO | ||
- TAGS | ||
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### Columns Droped are : | ||
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- URL | ||
- IMAGES | ||
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### Columns Created : | ||
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- YEAR | ||
- MONTH | ||
- Sentiments | ||
- Sentimental Score |
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Narendra Modi - Text Speeches Analysis/Dataset/modi_speeches.csv
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Narendra Modi - Text Speeches Analysis/Images/Most common words from speech.png
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# NAREDRA MODI TEXT SPEECH ANALYSIS | ||
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### 🎯 Goal : This project is to analyze the speeches of Narendra Modi. | ||
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### 🧵 Dataset : https://www.kaggle.com/adiamaan/modi-speeches | ||
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### 🧾 Description : | ||
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- Objective: The key idea of this analysis is to extract meaningful insights from the | ||
speeches of a prominent political figure. The analysis aims to understand the themes, keywords, frequency trends, and sentiments expressed in the speeches. | ||
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- Method: The analysis is conducted in four parts such as, Tag Frequency Analysis, Keyword Analysis, | ||
Speech Frequency Analysis and finally Sentimental Analysis. | ||
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- Outcome: Through these analyses, we gain a comprehensive insight into the content of the speeches | ||
and the thought process of Narendra Modi. The results highlight the main themes, commonly used keywords, trends in speech frequency, and the positive sentiment consistently present in the speeches. | ||
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### 🧮 What I had done!(Analytics) : | ||
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- Tag Frequency Analysis: Plotted the frequency of the 30 most popular tags on a bar graph, highlighting key | ||
government goals such as Good Governance, Infrastructural Development, Promoting Digital India Campaign, and other government schemes. | ||
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- Keyword Analysis: Analyzed the key words mostly included in the speeches, revealing a focus on India, | ||
Economy People, World friends, Productional growth, and Welfare. | ||
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- Speech Frequency Analysis: Analyzed the frequency of speeches over the years and months, | ||
showing a peak in 2019 due to the general election, with over 300 speeches. | ||
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- Sentiment Analysis: Assessed the sentiment of the speeches, | ||
finding that all were positive with scores greater than 0.9. | ||
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### Library used : | ||
- Pandas | ||
- Numpy | ||
- String | ||
- Matplotlib | ||
- Seaborn | ||
- Word cloud | ||
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### 📊 Visual Plots : | ||
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- ![](/Narendra%20Modi%20-%20Text%20Speeches%20Analysis/Images/Most%20common%20Tags.png) | ||
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- ![](/Narendra%20Modi%20-%20Text%20Speeches%20Analysis/Images/Word%20cloud.png) | ||
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- ![](/Narendra%20Modi%20-%20Text%20Speeches%20Analysis/Images/Most%20common%20words%20from%20speech.png) | ||
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- ![](/Narendra%20Modi%20-%20Text%20Speeches%20Analysis/Images/Number%20of%20Speeches%20per%20Year.png) | ||
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- ![](/Narendra%20Modi%20-%20Text%20Speeches%20Analysis/Images/Number%20of%20Speeches%20per%20Month.png) | ||
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### ✒️ Contributor : | ||
*Harsh Raj* | ||
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*Abhishek Sharma* (Mentor) |
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Narendra Modi - Text Speeches Analysis/Models/narendra-modi-speech-analysis.ipynb
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matplotlib==3.5.2 | ||
numpy==1.19.2 | ||
pandas==1.4.3 | ||
nltk==3.8.1 | ||
seaborn==0.11.2 | ||
vaderSentiment |
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