Digital Marketing Blog Search Engine Optimization Web Page Ranking: How Useful Is Google’s New Sentiment Analysis? Jeethu Vijayan July 22, 2020 Google interprets and analyzes text data to identify the underlying sentiments. This understanding enables google to sense the brand and its content. Businesses use this technique to identify customer sentiment toward products, brands, or services in online conversations and feedback. HIgh volumes of unstructured text data produced like emails, articles, social media conversations, documents, etc. are very hard to analyze and sort through. It is a time-consuming process for any algorithm. The sentiment of content improves its rankability according to google experts. Ranking based on sentiment is not possible but it indicates the diversity of content that leads to the actual ranking of a web page. Sentiments can be negative and positive, but it is used only to sway google and the customer to experience the brand’s character. Sentiment analysis is not considered a common criterion as it may not be domain-specific. The content or a word can have different meanings depending on the domain it represents. Google does not have a bias for a particular sentiment in the search context. Google analyzes the intent in the search and provides similar searches for the query. If you are run an SEO Agency, definitely you will tracking for the sentiment analysis, which is “Google Update.”. Although scientists have been trying to develop accurate means to classify content based on sentiment, it never seemed to be an appropriate method partly because of the intent, tone, and subjectivity that may vary from person to person and object to object. Analyzing the context is still a problem existing with the web page searches and subsequent rankings. The underlying factor is whether google can identify negative and positive sentiment in the searched context. To a certain extent, yes, Google can do this as it does not promote negative sentiment. The understanding of the subject is based on the positive information it is sharing and the authenticity of the shared information. Yet another challenge is defining the neutrality of the sentiment and ensuring the accuracy of the finding. The sentiment analysis model requires a consistent and definite criterion for underscoring the type of content and the context. It’s an exceedingly difficult task for google as it is for us, human beings. Sentiment analysis classifiers might not be as precise as other types of classifiers. A considerable amount of effort is put into this method and the question always arises whether it is worth it. Chances are that sentiment analysis predictions will be wrong from time to time, but it certainly is worth viewing the higher probability of getting it right. For everyday uses, such as ticket routing, task monitoring, and analysis, social media monitoring, brand monitoring, customer feedback, survey responses, and customer service sentiment analysis can be employed on a much larger scale than anticipated provided the tool of choice is compliant and compatible.