Our Customer Success Stories

Repustate has helped organizations worldwide turn their data into actionable insights.

Learn how these insights helped them increase productivity, customer loyalty, and sales revenue.

See all Stories

Table of Contents

Role of Data Cleaning in Sentiment Analysis

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.

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.

A company can choose amongst three sentiment analysis types, keeping in mind whichever best suits its objective and industry.

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.

While it may seem like an easy task when you are manually editing a couple of hundred comments, for example, TikTok insight but when you have to assess numerous videos, say when doing an Instagram analysis, such scenarios mean videos with an aggregated sum of comments running into thousands. In such a case, you need an automated sentiment analysis tool. However, for that tool to give you accurate and high-precision results, you need to make sure that you have a high-quality dataset prepped for analysis.

Incorrect sentiment analysis data preparation affects the algorithm and leads to incorrect analysis, even though at first glance it may all look in order. On the business front, this could mean a loss not only in resources and man-hours but also the risk of an expensive campaign gone wrong because it was built on incorrect insights.

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

You should scan your dataset and delete anything that you think is irrelevant or duplicated. Removing duplicated data is very important especially if you are collating data from multiple sources either by using a web scraper, say for news monitoring, or if you are using your CRM tool and therefore gathering inter-department customer data. This could also be the case where you are analyzing multiple videos based on a hashtag or keyword for Instagram social listening.

Step 2: Remove irrelevant items

Now that you have removed all duplicate data, you need to remove all items that are not relevant to your business objective. For this, you need to be properly aligned with the marketing goals for which you are conducting data cleaning in sentiment analysis. For example, if you are emotion mining for a voice of the customer analysis for a particular product line or age-group-based purchase behavior, you need to remove any data that is not relevant to that particular objective.

This means that even as you clean all your data manually and prepare the .csv file, ideally, you will have to conduct a separate analysis for each aspect of your business. This may seem like a long process but Repustate’s sentiment analysis platform, Repustate IQ, solves this challenge by seamlessly categorizing all the data with the help of semantic clustering and giving aspect-based sentiment scores automatically for each data group or aspect in the data.

Step 3: Check for outlier data

Outlier data is one that is at the extreme ends of the median, meaning it may be either too low or too high. If it looks like an improper data entry, you have to be the judge of whether you want to remove it or leave it. Removing outliers may generally help in ensuring better insight results as the outlier may be just irrelevant data, but it is not necessary to do so. It is better to observe and then take the action of either deleting them or letting them stay on in the dataset. Get further details about the sentiment analysis datasets.

Step 4: Correct typos and structural mistakes

This may seem like a simple task while data cleaning in sentiment analysis but is a very important one. Once all the data has been sieved and you’ve got the first cut of the actual data you will be analyzing, now is the time to correct any structural mistakes such as typos and inconsistencies in capitalizations, nomenclature, abbreviations, etc.

Step 5: Check for missing data

Missing data is never a good scenario but is not uncommon. You can solve this issue by either just removing all missing values from your analysis, or you can add a missing value based on assumption. Ofcourse, neither case is ideal but in the absence of data, they can be considered as options.

Step 6: Validate your data

Once you have undertaken all of the above-mentioned sentiment analysis data preparation steps, it is time to take stock of the end result that you have in front of you. Verify and validate your data to see if it makes sense in context of the overall objective of your marketing requirement.

You can check to notice if there are any data deviations from the prescribed rules for its field as well. You must also verify if the analysis is giving you any trends that are not in sync with what you determined, which could mean that the quality of the data needs to be better. For example, this could be the case with surveys, so you may need to revisit your questionnaire.

Discover More: Complete Sentiment Analysis Process


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.