The Role of Data Cleansing in Twitter Sentiment Analysis

Data cleansing in sentiment analysis is the process of filtering the datasets and removing incorrect, duplicate, or missing values to enhance data quality. If the datasets are not properly cleaned, they might interfere with the analysis, leading to distorted results.

A well-prepared dataset not only enhances machine-learning model performance but also empowers organizations to make informed decisions based on the results of sentiment analysis.

This blog explores the importance of data cleansing in Twitter sentiment analysis and discusses different data cleansing techniques for preparing datasets for analysis.

Understanding Twitter Sentiment Analysis

Twitter sentiment analysis is an automated machine-learning technique that analyzes the sentiment expressed in a tweet. It allows businesses to track what and how customers say about their products on social media. The technique can help you identify positive and negative mentions of your product and frustrated customers, too. 

In the example below, the tweet indicates the positive sentiment of an Amazon customer impressed by its fast delivery service.  

Twitter sentiment analysis can be effectively used in the following fields:

1. Market Research

Brands use Twitter sentiment analysis to get insights into consumer interests, preferences, feelings, and opinions about different topics. Understanding consumer likes, behaviors, and patterns helps in planning a product launch more effectively.

For instance, Twitter sentiment analysis was used to track consumer preferences by analyzing a sample of 3919 halal food tweets. A descriptive statistical analysis was performed to find out a generally positive sentiment towards halal food. 

2. Brand Reputation Monitoring

Twitter sentiment analysis can be used to track public opinion on newly launched products or marketing campaigns. It helps understand how well the product or the campaign resonates with the audience. 

For instance, P&G used Twitter sentiment analysis after launching their Gillette campaign, ‘The Best A Man Can Get.’ Though the campaign received mixed public opinions, the analysis revealed more positive responses than negative ones.

3. Customer Satisfaction Measurement

Reviews on social media reveal a lot about customers’ opinions of your brand and your product's strengths and weaknesses. Thus, they help you improve your products and services. 

For instance, an airline company used sentiment analysis to analyze online reviews about its brand. The analysis revealed mostly negative sentiments about the airline's timings and delays. 

Though Twitter sentiment analysis can be used in different fields, analyzing Twitter data due to its unstructured nature is quite challenging. Here are the major challenges in Twitter sentiment analysis.

One major challenge here is to identify the subjective texts where the same word can have different meanings based on context. Hence, it becomes challenging to analyze the sentiment accurately.

Moreover, there may be several opinions expressed in a tweet, making it complex to analyze. Here, advanced techniques must be used to extract accurate insights. 

It is crucial to use data cleansing techniques to ensure accurate results of Twitter sentiment analysis. Twitter data being unstructured requires data cleansing as it eliminates the different forms of noise in a tweet, like special characters, emojis, URL links, and @mentions.

These elements can distort the sentiment analysis results, and hence, data-cleansing solutions must be implemented to retain only relevant information to achieve accurate outcomes.

3 Common Issues in Twitter Sentiment Datasets

As of 2020, 54% of companies used sentiment analysis to understand customer sentiments, and it was predicted to grow to 80% in 2023. 

Despite the growing demand for Twitter sentiment analysis, brands face three common issues when analyzing Twitter datasets.

1. Noisy Text

Tweets often include typos, emojis, abbreviations, special characters, and short spellings that hinder the accuracy of sentiment analysis.

For example, using ‘u’ instead of ‘you’ or abbreviations like ‘LOL’; emojis and typos like ‘I Luv these products😍’ might be misinterpreted, leading to inaccurate sentiment analysis results.

2. Missing Data 

Missing values in Twitter sentiment datasets is another issue companies face. However, there are strategies to address this issue, like substituting missing values with the mean/ average values or algorithms to handle missing data. For instance, replacing missing values in datasets with the mean sentiment score of similar tweets.

3. Duplicate Data

Duplicate values in datasets can skew sentiment analysis results. Hence, it is critical to remove duplicates to ensure the results aren’t biased. Data cleansing can help remove duplicate tweets to prevent over-representation of specific sentiments and obtain more reliable results.

Data Cleansing for Twitter Sentiment Dataset Using Python

The importance of data cleansing in Twitter sentiment analysis cannot be denied. There are several data cleansing best practices and techniques companies use to ensure an accurate and reliable analysis of the tweets.  

1. Text Cleaning

This data cleansing technique involves eliminating special characters, URLs, @mentions, and hashtags from the tweets, which helps prevent distortion.

For example, the use of special characters and HTML tags is common in web-based text. You can use the following command to eliminate or replace them with spaces.

Removing Duplicates and Irrelevant Data

To improve data relevance, prevent skewed results by identifying and removing duplicate and irrelevant entries.

For example, remove redundant customer email IDs and customer age (irrelevant data) when analyzing their email addresses. 

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Standardizing Text

This data cleansing technique ensures uniformity and readability in the datasets. It involves converting text to lowercase, consistent capitalization across text data, removing stop words like 'is' and 'the,' and performing lemmatization to better understand the emotional tone and meaning behind the tweet.

For example, you can use Simplify Text Processor in Dataiku to remove stopwords, convert text to lowercase, alphabetic sorting, etc.

Handling emojis and emoticons

Convert emojis and emoticons in tweets to their text equivalents to boost the accuracy of sentiment analysis. Using the textual descriptions of emojis can help understand the emotional content of social media posts. 

In Python, converting emojis into text requires using the 'emoji' library and the emoji. demojize () function. Here's an example.

Data Cleansing Steps in Pandas

Now that you are aware of the different data cleansing techniques, here are the data cleansing steps you can implement using Pandas – a Python data analysis library offering functions like data cleansing techniques, exploring, and manipulating datasets.

1. Load the dataset

Use Pandas' read_csv()’ function to load the dataset.

2. Explore dataset

Explore datasets using the info() function to understand the data structure, number of non-null values, and memory usage. 

3. Remove missing values

If you identify any missing values in the dataset, use the following command to handle them. 

4. Remove duplicate data 

Avoid distorting the sentiment analysis by removing duplicate data from the datasets.

5. One-hot encoding

Perform one-hot encoding for categorical analysis to convert the categorical variables into binary (0 and 1) format.

6. Normalize float value columns

Perform the normalization process to scale numerical features and have a standard deviation of 1 and a mean of 0. This process ensures that all features equally contribute to the analysis.

7. Save the clean dataset

Once you have performed the data cleansing steps, save the cleaned dataset to the new CSV file.

Guidance on Handling Large Twitter Datasets

Typically, data cleansing techniques are easy to implement in Twitter sentiment analysis. However, if the Twitter datasets are large, it is crucial to handle them properly to optimize processing time and resources efficiently. Here are a few key strategies.

• Parallel Processing: Split your Twitter datasets into smaller parts and distribute the processing operations across multiple units. Use technologies like distributed databases or multiprocessing to achieve parallel processing.
• Index Optimization: For accelerated processing and updates, review and update your indexes based on the updated patterns of the Twitter datasets.
• Bulk Operations: Databases like SQL have a ‘bulk update’ option to speed up mass updates. These operations help optimize the processing of large datasets, enhancing performance.

Evaluation and Validation

Evaluate and validate the techniques after the customer data cleansing process is implemented to ensure reliable analysis and informed strategic decision-making.

The following are a few methods to validate the data cleansing process to enhance accuracy and operational efficiency.

1. Visualize Data Distributions

Visualize data distributions before and after implementing the data cleansing services using histograms, scatterplots, and box plots. This helps detect outliers, trends, and patterns that might impact the quality of data.

For example, visualize the relationships between sentiment scores and other variables like user engagement using scatterplots. You can also identify correlations and patterns between sentiment scores and specific dataset features.

2. Check Summary Statistics

Compare the before and after data cleansing summary statistics to identify major changes indicating inconsistency or error in the datasets. 

For example, you can use histograms to observe the sentiment score distribution before and after implementing data cleansing techniques in your Twitter datasets. This will help you spot changes in the scores after cleaning the data.

3. Use Data Validation Tools

Implement tools like Pandas-profiling to check data structure, quality, and statistics automatically before and after data cleansing.

For example, using Pandas-profiling, you can generate reports to analyze data structure, statistics, and data quality of raw Twitter datasets. Post-data cleansing, you can run automated evaluations on the datasets to assess their change in quality, structure, and statistics.

Conclusion

Twitter sentiment analysis is here to stay. More and more companies are adopting sentiment analysis to extract accurate insights into customer data. 

By implementing data cleansing techniques, such as removing irrelevant texts, missing values, and duplicates, companies can significantly improve the quality of the datasets being analyzed.

Using appropriate data cleansing solutions and tools, companies can tap into Twitter datasets' highest potential for sentiment analysis. Hence, companies can derive actionable insights from sentiment analysis, improving their decision-making. 

Want to enhance the quality of your datasets?

MarkovML can help you quickly analyze your datasets, detect missing values/rows and duplicate data, and guide you through the data cleansing process before model training. Its robust platform helps you obtain results more quickly, streamline data organization, expedite data evaluation, and simplify data analysis.

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