Have you ever thought about how customer ratings affect a company's success?
Let's explore what makes a ride-hailing app stand out. Join me as we dive into the world of customer feedback to understand why people love Ola and Uber.
Introduction to the topic of Sentiment analysis
Sentiment analysis is like a magnifying glass for understanding what customers truly feel about a company. It sifts through all the reviews and comments people leave online to figure out whether they're happy, disappointed, or somewhere in between.
Objective: This blog aims to compare the performance of two leading companies, Ola and Uber, through sentiment analysis of customer reviews. By delving into the sentiments expressed by users, we seek to identify areas where each company excels and where improvements can be made. Ultimately, the goal is to provide insights that can help both companies enhance their strategies and stay ahead in the competitive market landscape.
We are going to play with the data and answer the questions.
1. Does ratings determine the sentiment scores?
2. How do the overall sentiment scores compare between Ola and Uber?
3. What are the most common reasons for positive and negative feedback for both Ola and Uber?
Data collection: The dataset was sourced from Kaggle, a popular platform for accessing and sharing datasets. This dataset was chosen for its relevance to the analysis objective, which focused on understanding ola and uber reviews.
Data Preprocessing: Before we could analyze the reviews, we had to clean and prepare the data. This involved several steps to ensure our analysis would be accurate. We started by removing any duplicate entries and filling in missing values where needed. Then, we focused on text preprocessing, which included converting all text to lowercase, breaking it into individual words (tokenization), removing punctuation, stop words, and special characters, and reducing each word to its base form through lemmatization. Finally, we transformed the tokens back into readable text for analysis. This rigorous preprocessing ensured that our data was ready for meaningful analysis.
Methodology: After preprocessing the data, we moved on to the next step: model building. For sentiment analysis, we utilized the SentimentIntensityAnalyzer from the Natural Language Toolkit (NLTK) library. This model allows us to analyze the sentiment of text data and assign scores indicating the positivity or negativity of each review. We implemented the model and ran it on our preprocessed data to extract insights and assess the sentiment of customer reviews for both Ola and Uber.
Analysis: Now, let's see what our model tells us about Ola and Uber based on the reviews.
We had a lot of reviews to work with: over 850 for Ola and more than 1000 for Uber. This gives us a good amount of data to understand how customers feel about each company. We'll look at the scores our model gives for these reviews to figure out how well each company is doing and how satisfied customers are.Analysis of Ratings:
Looking at the ratings provided by customers, we notice that the majority of ratings fall into two categories: 1 and 5. It's interesting to see that both Ola and Uber have a significant number of ratings at the extremes. This suggests that customers have strong opinions about the apps, either expressing dissatisfaction or satisfaction.
Analysis of Average Ratings:
Interesting, Ola has lower ratings compared to Uber. Ola's average rating stands at 1.8, while Uber boasts a 4.0 rating. This suggests that Uber has been more successful in satisfying its customers compared to Ola.
Analysis of Ola's sentiment:
Analysis of Ola's sentiment:
Surprisingly, there are more negative sentiments expressed by customers for Ola. It's crucial for the company to address these issues and strive for positive feedback. Additionally, there are over 225 neutral sentiments, indicating customers are somewhere between negative and positive feedback. Ola should focus on improving its positive ratings to enhance customer satisfaction.Analysis of Uber's sentiment:
The numbers show that more people are happy with Uber than with Ola. Uber has fewer unhappy customers compared to Ola, which is a good sign for the company. However, there's still a group of people who haven't made up their minds, sitting in the middle between happy and unhappy.1. Does ratings determine the sentiment scores?
Analysis of Ola's ratings :
It's surprising to discover that some customers who gave 5-star ratings also left negative and neutral feedback. This suggests that relying only on ratings for analysis may not provide the full picture of customer satisfaction.
Analysis of Uber's ratings :It's surprising to find a similar pattern in Uber reviews. Even though customers give high ratings of 4 or 5 stars, their feedback can still contain negative or neutral sentiments. This shows that high ratings don't always mean positive reviews, which is quite unexpected.
2.How do the overall sentiment scores compare between Ola and Uber?
Analysis of Ola and Uber's ratings:
this plot shows both companies performance together. Ola tends to have more negative feedback, while Uber receives more positive feedback. Additionally, there are customers who express neutral sentiments for both companies.
3.What are the most common reasons for positive and negative feedback for both Ola and Uber?
In our analysis, we went deeper into the positive sentiments expressed by customers for both Ola and Uber.
Here are the top 10 most common words found in positive reviews for each company:
These insights shed light on the aspects of each service that customers appreciate the most.For Ola, customers often mention the app's convenience, good drivers, and responsive customer support.
On the other hand, Uber customers praise the overall service quality, the ease of use of the app, and the professionalism of the drivers. Such insights can help both companies tailor their services to meet customer expectations and enhance overall satisfaction.
In our analysis of negative reviews, we identified the top 10 most common reasons cited by customers for both Ola and Uber:
In summary:
Ola:
1. Driver: 256 mentions, indicating issues with driver behavior or professionalism.
2. App: 190 mentions, suggesting usability issues or glitches in the Ola app.
3. Ride: 191 mentions, highlighting problems with ride experiences like delays or route issues.
4. Worst: 154 mentions, expressing strong dissatisfaction with Ola's service.
5. Service: 119 mentions, indicating problems with customer service or support.
Uber:
1. Driver: 85 mentions, signaling driver-related concerns impacting satisfaction.
2. App: 58 mentions, suggesting usability issues with the Uber app.
3. Uber: 53 mentions, indicating broader dissatisfaction with the brand or service.
4. Ride: 51 mentions, similar to Ola, indicating ride-related issues.
5. Time: 39 mentions, highlighting dissatisfaction with wait times or delays.
By understanding these common issues, both companies can focus on addressing specific negative points to improve the overall customer experience and satisfaction levels.
These highlight the key negative points experienced by customers when using both Ola and Uber services. Common issues such as driver behavior, app functionality, service quality, and time management are areas where both companies may need to focus on improvement to enhance customer satisfaction and loyalty.
Conclusion:
In conclusion, our analysis of customer reviews for Ola and Uber revealed valuable insights into the performance of these ride-hailing companies. Despite Ola's lower average ratings compared to Uber, both companies face similar challenges, such as issues with drivers, app usability, and ride experiences. However, Uber generally received more positive feedback, indicating higher overall customer satisfaction. By addressing common pain points highlighted in negative reviews, both Ola and Uber can enhance their services and better meet customer expectations, ultimately staying competitive in the market.
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