Sentiment Analysis Data sources for Strategic Text Analysis
Tags: textanalysis, sentimentanalysis, emotionanalysis, machinelearning
Published: 2022-03-29 05:51:10

Sentiment analysis or opinion mining is a natural language processing (NLP) technique used to analyze text data. It analyzes textual data to determine whether it is positive, negative, or neutral. Brands and businesses often use sentiment analysis to analyze public opinion about their brand or product sentiment via customer feedback. It also helps them understand the current market trends and needs of the customers.

**What are the sources of gathering sentiment analysis data?**

The efficient way to build an effective [sentiment analysis](https://www.bytesview.com/sentiment-analysis) solution is, by analyzing various datasets and testing different approaches. You will need to accumulate a substantial volume of data to perform your research and testing, you can either gather the data by yourself from sources like review, social media, employee interaction data, etc or you can take help from sentiment analysis APIs like **[Bytesview](https://www.bytesview.com/)** which gathers text data from multiple sources (reviews, opinions, suggestions, social posts, support queries, etc ) and transform it into actionable insights to help make data-driven decisions.

Let’s discuss each source of gathering sentiment analysis data in detail -

**Customer Reviews-** Sentiment analysis data can be gathered from review websites such as Superpages, Google reviews, Clutch, Demandforce, etc. You can further get narrow down your data search specific to an industry such as Glassdoor, Vault, JobAdvisor, for employee satisfaction and hospitality; Yelp, Yahoo! Local, etc for restaurants and local businesses.

**Videos and News Articles-** Videos and articles from news websites are apt sources of sentiment analysis data for various business needs, especially brand reputation monitoring. An organization can compile all relevant videos in its repository and analyze them with the help of video AI.

**Social Media-** Sentiment analysis data sources can be gathered from social media including platforms such as YouTube, TikTok, Twitter, Tumblr, Instagram Live, WeChat, Youku, Reddit, and others. Interestingly, relevant information can be extracted from not only comments but the videos themselves through social media video content analysis.

**Surveys-** Surveys are one of the best sentiment analysis data sources to get meaningful data. Surveys can be sent by chats, emails, mobile messages, and even phone calls. Open-ended survey questions tend to allow people to express themselves more freely, so that’s where the major chunk of your insights will come from.**[ Bytesview](https://www.bytesview.com/)** even integrates its sentiment analysis API with survey software to provide sentiment analysis-driven survey intelligence.

Insights from sentiment analysis data sources help a business beyond measures. Sentiment analysis NLP (natural language processing) of this data is vital for formulating necessary corporate and marketing strategies.

If done right, sentiment analysis can be crucial to a company in the following ways-

1. Capturing new markets
2. Competitor analysis
3. Employee engagement
4. Product differentiation
5. Business intelligence
6. Better sales conversions
7. Capturing new markets
8. Increase brand value and visibility

**Wrapping Up**

Information from the right sentiment analysis data sources can give organizations a deeper understanding of their target customers, which can translate into more profitable business strategies. Bytesview’s sentiment analysis platform analyzes such data in more than 30+ languages and dialects and gives you valuable insights.