{"id":51245,"date":"2020-12-09T14:22:12","date_gmt":"2020-12-09T14:22:12","guid":{"rendered":"https:\/\/www.sotrender.com\/resources\/?p=7823\/"},"modified":"2020-12-09T14:22:12","modified_gmt":"2020-12-09T14:22:12","slug":"what-is-sentiment-analysis","status":"publish","type":"knowledge_base","link":"https:\/\/www.sotrender.com\/blog\/knowledge_base\/what-is-sentiment-analysis\/","title":{"rendered":"What is sentiment analysis in social media?"},"content":{"rendered":"

Social media managers and marketers usually gauge how their social media strategy is going by checking metrics like engagement, mentions, and follower growth. However, these metrics don\u2019t reveal anything about <\/span>how your audience feels about your brand or your services<\/b>.<\/span><\/p>\n

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Sentiment analysis<\/b> in social media will help you gather information about your clients\u2019 or fans\u2019 feelings and perception. The general idea is that we want to analyze a piece of text, potentially a status update or a comment. We then want to create a system that can answer:<\/span><\/p>\n

Did this user write something negative, neutral, or positive?<\/span><\/i><\/p>\n

In order to find out the answer to this question, we need a program that can extract meaning from text.<\/span><\/p>\n

How do you analyze the sentiment of social media posts?<\/strong><\/h2>\n

The first step of sentiment analysis is to transform the whole text to numbers so that it is easier for the machine to understand. This means that similar words and\/or sentences should have similar numeric representations. To learn more about how this is done, we recommend you check out <\/span>Google\u2019s crash-course on embeddings<\/span><\/a>.<\/span><\/p>\n

After we have the numeric representation of a sentence, we still need an algorithm (for example, neural networks). Once we have a ready algorithm, we\u2019ll have the final answer about the sentiment. However, we now need to teach the algorithm what sentiment is. To do that, we need to feed the algorithm text with sentiment labels.<\/span><\/p>\n

By the end of it, the model should be able to generate output similar to this:<\/span><\/p>\n

\"\"<\/p>\n

What are some situations where sentiment analysis is especially useful?<\/strong><\/h2>\n

Without going into too much detail, here are just some of the ways that sentiment analysis can be helpful in managing social media profiles.<\/span><\/p>\n