Role of Data Cleaning in Sentiment Analysis

2. Role of Data Cleaning in Sentiment Analysis

3. Overview Data cleaning in sentiment analysis is the process of removing redundant and incorrect values in data that is meant for analysis. This is a necessary step in the sentiment analysis process, whatever the business requirement may be - whether customer experience analysis, employee satisfaction analytics, or brand experience insights. Removing all the unnecessary data items that do not belong in your dataset is an essential part of sentiment analysis data preparation, without which the insights you receive will be inaccurate and cannot be relied on.

4. What Is Sentiment Analysis? Sentiment analysis is the machine learning-based process of extracting sentiment or emotion from a given dataset. Several machine learning techniques are used for emotion mining, which include natural language processing, semantic analysis, computational linguistics, and so on. Since AI is used to automatically detect positive and negative emotions in data, it makes data cleaning in sentiment analysis a very important criterion. In a business application, sentiment analysis allows companies to obtain greater insights into the minds of their audience so that they can formulate better campaigns and strategies for the end objective. For example, a company may want to use customer experience analysis for enhanced product innovations and improving sales conversions. Or, a human resources team may want to use sentiment analysis to ensure they build better, more relevant policies for improved employee satisfaction and engagement.

5. Why Do You Need Data Cleaning In Sentiment Analysis? Sentiment analysis allows you to mine emotions from data at scale and with precision. This is not to say that there are no sentiment analysis challenges. However, these can be easily navigated by ensuring that you have the right sentiment analysis platform and that data cleaning in sentiment analysis has been performed to the optimal level. Once these two aspects are taken care of, you can gather whatever insights you are looking for - whether from social media listening or mining Google and Amazon reviews for product research. However, to get to that point where you can conduct sentiment analysis, you need to make sure that your data is pristine. In data analytics terms, this means that there is no duplicate, incorrectly formatted, incomplete, corrupted, or simply, wrong data, in your dataset.

6. How Can You Do Data Cleaning? There are several measures that you can take to conduct data cleaning in sentiment analysis depending on the characteristic of the dataset you have. You can, however, establish a methodical approach such as below for sentiment analysis data preparation, and then use the same steps for all your future projects. Step 1: Delete duplicate data Step 2: Remove irrelevant items Step 3: Check for outlier data Step 4: Correct typos and structural mistakes Step 5: Check for missing data Step 6: Validate your data

7. Conclusion By taking all the above steps you can make sure that your dataset is cleaned and prepped for analysis through thorough and systematic data cleaning in sentiment analysis. This way you are assured of high quality insights which can thus act as the backbone of your organization’s business strategies. Repustate’s sentiment analysis platform Repustate IQ, also available as an API, allows you to upload data manually as well as by sourcing it directly from various data sources such as social media, reviews, news articles, and more. It can natively analyze 23 languages, and offers unmatched insights through its numerous aspect models catering to hospitality, banking, retail, healthcare, and more.

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