Twitter-Sentiment-Analysis

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Twitter-Sentiment-Analysis

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Twitter-Sentiment-Analysis 최신버젼 다운로드

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# Twitter-Sentiment-Analysis

**Summary**
- Got a Twitter dataset from Kaggle
- Cleaned the data using the tweet-preprocessor library and the regular expression library
- Splitted the training and the test data by 70/30 ratio
- Vectorized the tweets using the CountVectorizer library
- Built a model using Support Vector Classifier
- Achieved a 95% accuracy

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**Data Collection**

- Link to the dataset: https://www.kaggle.com/arkhoshghalb/twitter-sentiment-analysis-hatred-speech

1. Understanding the dataset
 - Let's read the context of the dataset to understand the problem statement. 
 - In the training data, tweets are labeled '1' if they are associated with the racist or sexist sentiment. Otherwise, tweets are labeled '0'. 
2. Downloading the dataset
 - Now that you have an understanding of the dataset, go ahead and download two csv files - the training and the test data. Simply click "Download (5MB)."

**Data Exploration (Exploratory Data Analysis)**
- Let's check what the training and the test data look like.
 
 ![](https://raw.githubusercontent.com/importdata/Twitter-Sentiment-Analysis/master/images/1.png)

- Notice how there exist special characters like @, #, !, and etc. We will remove these characters later in the data cleaning step. 
- Check if there are any missing values. There were no missing values for both training and test data.

![](https://raw.githubusercontent.com/importdata/Twitter-Sentiment-Analysis/master/images/2..png)
  
  ![](https://raw.githubusercontent.com/importdata/Twitter-Sentiment-Analysis/master/images/3.png)
  
**Data Cleaning**
- We will clean the data using the tweet-preprocessor library. Here's the link: https://pypi.org/project/tweet-preprocessor/
- This library removes URLs, Hashtags, Mentions, Reserved words (RT, FAV), Emojis, and Smileys.
- We will also use the regular expression library to remove other special cases that the tweet-preprocessor library didn't have.
  
  ![](https://raw.githubusercontent.com/importdata/Twitter-Sentiment-Analysis/master/images/4.png)
  
**Train and Test Split**
- Now that we have cleaned our data, we will do the test and train split using the train_test_split function.
- We will use 70% of the data as the training data and the remaining 30% as the test data.

**Vectorize Tweets using CoutVectorizer**
- Now, we will convert text into numeric form as our model won't be able to understand the human language. We will vectorize the tweets using CountVectorizer. CountVectorizer provides a simple way to both tokenize a collection of text documents and build a vocabulary of known words. 
- For example, let's say we have a list of text documents like below.
  document = ["This is Import Data's YouTube channel",
            "Data Science is my passion and it is fun",
            "Please subscribe to my channel"]
            
- CountVectorizer combines all the documents and tokenizes them. Then it counts the number of occurrences from each document. The results are shown below.
  ![](https://raw.githubusercontent.com/importdata/Twitter-Sentiment-Analysis/master/images/5.png)
            
**Model Building**
- Now that we have vectorized all the tweets, we will build a model to classify the test data. 
- We will use a supervised learning algorithm, Support Vector Classifier (SVC). It is widely used for binary classifications and multi-class classifications.
- You can find more explanation on the scikit-learn documentation page: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html

**Accuracy**
- And here we go! The accuracy turned out to be 95%!
![](https://raw.githubusercontent.com/importdata/Twitter-Sentiment-Analysis/master/images/6.png)

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