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Social Media Monitoring > Clean Data and Accruracy

Clean Data & Higher Accuracy

Billions of social media data circulating on the internet every second.

As technology from social media monitoring tools can only capture a fraction of what is posted based on your keyword input, this leads to error in decision making.

Unfortunately, social media data is inherently filled with noise, i.e posts or mentions which are irrelevant to the organization, topic, issue, or brand that you are currently monitoring.

Each dataset is unique and inherently contains information that is not relevant to your organization or brand.

Our approach to data cleaning means more insights, leading to better decisions.

Data Error in Social Media

Data error stems from irrelevant data that needs to be removed or excluded from our analysis. Essentially, we need to reduce the data error from any datasets that we extract from social media (or any other datasets). The higher the data error rate, the higher the probability in making the wrong decisions.

Data preparation is the first step in any data analytics and often overlooked by inexperienced users who rely heavily on social media monitoring software.

In fact, obtaining a clean set of data forms an integral part of our social media monitoring services and reduces error for decision makers, strategic communication practitioners and professional campaign managers.

Here is what we found from our years of experience in data cleaning from social media monitoring:

  • more than 40% data error found in social media for companies with acronyms or generic names attached to their brands, such as “POS”, “PLUS”, “Connect”, “XL”, “MAS” and others.
  • between 20% data error found in social media for companies with unique brands or those searching for specific topics.
  • the exclusion functionality found in social media monitoring tools to remove unwanted posts is unable to remove the noisy data. We found 30-40% data error in most of social media data found for brands, companies and issue that they are tracking.

Gain higher accuracy from local dialects

75% of the global internet users communicate in their own native language. It means users uses dialects and slangs in their own native language to effectively communicate in multiple languages.

Add to the local dialects and acronyms with English language, this is an on-going challenge for automated social media monitoring tools and inexperienced users to gain the best insights when it comes to analyzing public perception or consumer sentiment.

For global companies with global presence in multiple countries, understanding nuances and sarcasm from the local language and dialects will bring greater competitive advantage to leverage on localized insights and consumer behaviour.

In fact, local domestic companies within the country experience similar challenges too. Take an example of a national postal company in Malaysia, known as POS Malaysia which operates more than 1,000 branches and offices in Malaysia.

Using the traditional approach of using software-only solution based on the keyword “POS”, social media monitoring will result in a huge volume of data that is not relevant to the brand. In most cases, the keyword “POS” can yield to Point-Of-Sales or something which is highly irrelevant.

Overcoming the challenges

Data cleaning is embedded as part of our social media monitoring services and solution. This means the output you will receive, either from daily, weekly, monthly, quarterly or even yearly reports will be error-free.

Our data cleaning will cover the following activities:

  1. Removal of data (i.e mentions or posts) which are not relevant to the topic, issue or brand – eg: classifieds, or posts from spam bots.
  2. Reclassification of positive and negative keywords that are unique for the domain or topic of interest in order to enhance the machine learning capabilities to analyze sentiment more accurately.


Our team extract and clean your datasets before analyzing using our proprietary text algorithms, SENTIROBO® and EMOROBO® to measure sentiment and emotions respectively. An example of the output from data cleaning will yield higher sentiment accuracy – beneficial for reputation risk managers and corporate communications practitioners to focus on future strategies.

Discover more value from social media monitoring and data analytics.

As communications in the digital world are getting more complex with millions of conversation in social platforms,  there are values to be unlocked when you begin with the right steps. We uncover more insights than your conventional tools or existing processes.

Drop us a note and we are more than happy to assist.