SENTIMENT ANALYSIS PADA APP TIKTOK #datascience #sentimentanalysis #machinelearning

Introduction

In this article, we will walk through the process of conducting sentiment analysis on user reviews from the TikTok app. This analysis involves various data preprocessing techniques, data visualization, and sentiment classification. Below is a detailed outline of the steps we follow.

Step 1: Data Preparation

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We begin with a dataset that contains various columns such as review ID, username, content, and score. For our analysis, we will focus primarily on the “content” and “score” columns. In this case, we will address how to create a new dataset using the existing one.

We need to ensure that there are no missing values within the score column. Upon checking, it was found that there are 16 entries with missing content data. However, the score data was intact. To mitigate this issue, we can remove the empty content data by using the dropna() function, which cleans our dataset for further analysis.

Step 2: Rating Visualization

Next, we analyze the ratings distributed from 1 to 5. To visualize this, we will create a pie chart using the plotly.express library, showcasing the percentage of each rating. After plotting the graph, we can see that the highest rating is a 5, representing 73.6% of the responses, while the rating of 1 is significantly lower.

Step 3: Word Frequency Analysis

Furthermore, we will investigate the types of words used in the reviews. We create a variable to store all content and utilize a loop to process and consolidate the vocabulary contained in user reviews. After processing, we will generate a word cloud that visualizes the most frequently used words; more frequently mentioned terms will appear larger in the cloud.

Step 4: Sentiment Classification

To enhance our dataset, we change the score column into three distinct categories: positive, negative, and neutral. This is done by performing sentiment analysis on the review data. Using a sentiment scoring algorithm, we will classify each review accordingly.

The transformed dataset consists of four columns: content, positive, negative, and neutral, although we will primarily use the positive and negative columns for further analysis.

Step 5: Positive and Negative Word Cloud

To conclude our analysis, we will generate word clouds for both positive and negative sentiment. The positive word cloud depicts words that are frequently associated with positive sentiments in the reviews, while the negative word cloud illustrates the words that signify dissatisfaction or negative feelings.

This structured approach allows us to achieve valuable insights into user sentiments surrounding the TikTok app, thereby contributing to better content strategies and user engagement practices.

Keyword

• Sentiment analysis
• TikTok reviews
• Data preprocessing
• Visualization
• Pie chart
• Word cloud
• Positive sentiment
• Negative sentiment

FAQ

1. What is sentiment analysis?
Sentiment analysis is the process of determining the emotional tone behind a series of words, used to understand the attitudes, opinions, and emotions expressed in a text.

2. Why is sentiment analysis important for apps like TikTok?
Understanding user sentiment can help app developers and marketers improve user engagement, develop better features, and create content that resonates with users.

3. How do I handle missing data in my dataset?
You can handle missing data by using functions like dropna() to remove rows with missing values or by imputing values based on other available data.

4. What libraries can I use for data visualization?
You can use libraries like matplotlib, seaborn, and plotly for data visualization tasks.

5. How do I create a word cloud?
You can create a word cloud using libraries like wordcloud in Python, which visualizes the frequency of words in your dataset by displaying them in varying sizes.