Natural Language Processing for Sentiment Analysis

Businesses need to leverage Natural Language Processing for sentiment analysis in order to better understand their customers. Let us discuss this statement in more detail to get a better understanding about how your business can benefit from the use of Natural Language Processing.

Social Data offers a tremendous opportunity to businesses to capture the opinions, sentiments, needs, and intent of their customers/or potential customers. Sentiment (both positive and negative) offer a significant insight about the opinion customer express via digital media. The digital media provides information that offers rich insights about consumer choices and, many times, their decisions. Thus, Natural Language Processing is the best ways to understand and uncover the voice of your customers.

If your organization can combine sentiment analysis with a powerful social media monitoring strategy you’ll be able to better anticipate your customers’ reactions and act accordingly, thereby improving your customer experience and customer loyalty. However, without NLP and right set of data it is difficult to discover and collect insights that are important to propel your business.

Many consumer product companies are using Natural Language Processing to extract social sentiments and have acknowledged the impact of social media on their brand and products.

However, the ways in which human communicate (both speech and text) is complicated. Considering the cultural variation, slang and sarcasm that occurs in blogs, comments, forums, and emails it is not easy for machines to learn and understand the natural language. Even more difficult is to teach machines about how context can affect tone. To give you an example, a machine just looks for target words, when context is not given, and will automatically categorize “wonderful” as positive and “bad” as negative.

However, given the above limitations, Natural Language Processing speech analysis techniques are widely used for part of speech tagging, named entity detection, and sentiment analysis. There is lot of research going on in this area and with technological advancements things are set for more efficient results and outcomes.

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