Benefits of Sentiment Analysis

Oct 13, 2025 by recoveryshake.com 84 views

Introduction

Gojek is one of the most popular online transportation service providers in Indonesia, which is the first choice for people with high mobility. However, the level of user satisfaction with Gojek services varies greatly, influenced by various factors such as service quality, mileage, speed, accessibility, cost, and security. One way the user expresses their opinions about Gojek services is through comments on Twitter. However, analyzing the opinions of thousands of tweets can be very time and energy. Therefore, sentiment analysis is needed as a method for extracting and analyzing opinions about a particular topic or service.

Sentiment analysis is a technique used to determine the emotional tone or attitude conveyed by a piece of text, such as a tweet. It is a crucial tool for businesses, like Gojek, to understand how their customers feel about their services. By analyzing the sentiment of customer feedback, businesses can identify areas for improvement and make data-driven decisions to enhance their services.

Methodology

This study aims to implement the Xgboost algorithm in classifying user sentiment to Gojek. The data used consists of 1,445 tweets with positive, negative, and neutral sentiment labels. Stages in data processing (preprocessing) include several key steps. First, casefolding is used to convert all letters into lowercase letters so that the analysis is more consistent. Then, the cleaning step is done to eliminate symbols and punctuation that are not needed in every tweet. The next process is the removal of the conjunction (stopwords removal) which aims to reduce noise in the data. Furthermore, the words are normalized and carried out stemming to take the basic form of each word.

At the stage of giving weight to words, this study uses TF-IdF Vectorizer, which serves to convert each word into numbers based on the frequency in the corpus. The classification step is carried out by the Xgboost method, which is then maximized through the arrangement of several hyperparameters. Evaluation of the analysis process is carried out using confusion matrix, and the results obtained indicate a very good level of accuracy, which is 92%.

Why is the Sentiment Analysis Important?

Sentiment analysis is very important in the context of customer service, especially for large companies such as Gojek. By understanding how users feel the services provided, companies can make better decisions to improve service quality. For example, if many negative tweets involve the speed of service, Gojek can conduct evaluations and improvements to the Dispatch system or increase the number of drivers at busy times.

Furthermore, this analysis also helps in understanding the trends and patterns of user behavior. Data obtained from sentiment analysis can be used to create a more effective marketing strategy and target the right customer segments. In addition, feedback from users obtained through this analysis also functions as a source of valuable information in the development of new products and services.

Benefits of Sentiment Analysis

The benefits of sentiment analysis are numerous. Some of the key benefits include:

Improved customer service: By understanding how customers feel about their services, businesses can make data-driven decisions to improve their services and meet customer expectations.
Enhanced marketing strategy: Sentiment analysis can help businesses create a more effective marketing strategy and target the right customer segments.
Increased customer loyalty: By responding to customer feedback and concerns, businesses can increase customer loyalty and retention.
Improved product development: Sentiment analysis can provide valuable insights into customer needs and preferences, which can be used to develop new products and services.

Conclusion

The implementation of sentiment analysis using the Xgboost algorithm in this study shows how strong modern data analysis tools are in understanding user experience. With a 92% accuracy, this study provides concrete evidence of the ability of Xgboost in classifying sentiments based on tweet data. Gojek, as one of the main actors in the online transportation industry in Indonesia, can take advantage of the results of this analysis to continue to improve services and meet customer expectations. Sentiment analysis is not just a tool, but is also the key to understanding and reaching users more effectively in the current digital age.

Future Research Directions

There are several future research directions that can be explored in the context of sentiment analysis on Gojek services. Some of these directions include:

Using other machine learning algorithms: Other machine learning algorithms, such as random forest and support vector machines, can be used to classify sentiments and compare their performance with Xgboost.
Analyzing sentiment on other social media platforms: Sentiment analysis can be extended to other social media platforms, such as Facebook and Instagram, to gain a more comprehensive understanding of user sentiment.
Using natural language processing techniques: Natural language processing techniques, such as named entity recognition and part-of-speech tagging, can be used to extract relevant information from text data and improve the accuracy of sentiment analysis.

Limitations of the Study

There are several limitations of this study that should be noted. Some of these limitations include:

Limited dataset: The dataset used in this study consists of only 1,445 tweets, which may not be representative of the entire population of Gojek users.
Limited scope: This study only analyzed sentiment on Twitter and did not consider other social media platforms or other types of text data.
Limited analysis: This study only analyzed sentiment and did not consider other aspects of user experience, such as user engagement and user retention.

Recommendations for Future Research

Based on the findings of this study, several recommendations can be made for future research. Some of these recommendations include:

Using larger and more diverse datasets: Future studies should use larger and more diverse datasets to improve the accuracy and generalizability of sentiment analysis.
Analyzing sentiment on other social media platforms: Future studies should extend sentiment analysis to other social media platforms, such as Facebook and Instagram, to gain a more comprehensive understanding of user sentiment.
Using natural language processing techniques: Future studies should use natural language processing techniques, such as named entity recognition and part-of-speech tagging, to extract relevant information from text data and improve the accuracy of sentiment analysis.

Q: What is sentiment analysis?

A: Sentiment analysis is a technique used to determine the emotional tone or attitude conveyed by a piece of text, such as a tweet. It is a crucial tool for businesses, like Gojek, to understand how their customers feel about their services.

Q: Why is sentiment analysis important for Gojek?

A: Sentiment analysis is important for Gojek because it helps the company understand how customers feel about their services. By analyzing the sentiment of customer feedback, Gojek can identify areas for improvement and make data-driven decisions to enhance their services.

Q: What is Xgboost?

A: Xgboost is a popular machine learning algorithm used for classification and regression tasks. It is known for its high accuracy and speed, making it a popular choice for sentiment analysis.

Q: What is the accuracy of the sentiment analysis model used in this study?

A: The accuracy of the sentiment analysis model used in this study is 92%. This indicates that the model is able to accurately classify sentiments with a high degree of accuracy.

Q: What are the benefits of sentiment analysis for Gojek?

A: The benefits of sentiment analysis for Gojek include:

Improved customer service: By understanding how customers feel about their services, Gojek can make data-driven decisions to improve their services and meet customer expectations.
Enhanced marketing strategy: Sentiment analysis can help Gojek create a more effective marketing strategy and target the right customer segments.
Increased customer loyalty: By responding to customer feedback and concerns, Gojek can increase customer loyalty and retention.
Improved product development: Sentiment analysis can provide valuable insights into customer needs and preferences, which can be used to develop new products and services.

Q: What are the limitations of the study?

A: The limitations of the study include:

Limited dataset: The dataset used in this study consists of only 1,445 tweets, which may not be representative of the entire population of Gojek users.
Limited scope: This study only analyzed sentiment on Twitter and did not consider other social media platforms or other types of text data.
Limited analysis: This study only analyzed sentiment and did not consider other aspects of user experience, such as user engagement and user retention.

Q: What are the recommendations for future research?

A: Based on the findings of this study, several recommendations can be made for future research. Some of these recommendations include:

Using larger and more diverse datasets: Future studies should use larger and more diverse datasets to improve the accuracy and generalizability of sentiment analysis.
Analyzing sentiment on other social media platforms: Future studies should extend sentiment analysis to other social media platforms, such as Facebook and Instagram, to gain a more comprehensive understanding of user sentiment.
Using natural language processing techniques: Future studies should use natural language processing techniques, such as named entity recognition and part-of-speech tagging, to extract relevant information from text data and improve the accuracy of sentiment analysis.

Q: How can Gojek use the results of this study?

A: Gojek can use the results of this study to improve their services and meet customer expectations. By analyzing the sentiment of customer feedback, Gojek can identify areas for improvement and make data-driven decisions to enhance their services.

Q: What are the implications of this study for the online transportation industry?

A: The implications of this study for the online transportation industry are significant. By using sentiment analysis to understand customer sentiment, companies like Gojek can improve their services and meet customer expectations, leading to increased customer loyalty and retention.

Q: What are the future directions for this research?

A: The future directions for this research include:

Using other machine learning algorithms: Other machine learning algorithms, such as random forest and support vector machines, can be used to classify sentiments and compare their performance with Xgboost.
Analyzing sentiment on other social media platforms: Sentiment analysis can be extended to other social media platforms, such as Facebook and Instagram, to gain a more comprehensive understanding of user sentiment.
Using natural language processing techniques: Natural language processing techniques, such as named entity recognition and part-of-speech tagging, can be used to extract relevant information from text data and improve the accuracy of sentiment analysis.