Sentiment analysis is contextual text analysis that identifies and extracts subjective information from the source material and helps a business understand the social sentiment of their brand, product or service while tracking online conversations. Sentiment Analysis, also known as Opinion Analysis, is a dedicated natural language processing application that helps determine if the data provided contains positive, negative or neutral sentiment.
This multi-level analytical approach provides insight into the sentiments of people, places, and things and the context behind those views. In customer service and call centre applications, sentiment analytics is valuable for tracking opinions and emotions among different customer segments.
How it works
This can be done using machine learning or vocabulary-based approaches. This type of analysis extracts meaning from many text-based sources such as polls, reviews, public social media, and even articles on the Internet.
Traditional sentiment analysis involves using a lookup dictionary to understand the positive degree of certain words and then calculating the average of these scores as the sentiment of the text. The dictionary-based method we are discussing determines the overall sentiment of a text by adding up the individual sentiment scores of each word in the text.
Several thematic sentiment lexicons are designed for use with the text of a specific area of content. The main activity in sentiment analysis is the classification of the polarity of a given text at the document, phrase, or function/aspect level, whether the opinion expressed in a document, phrase, or characteristic/aspect of an object is positive; negative or neutral.
A mood score identifies emotions and assigns them a score, for example, 0 to 10, from the most negative to the most positive feeling. Sentiment score is a scale system that reflects the emotional depth of emotion in a passage of text. Often, sentiment is calculated for a document as a whole, or some aggregation is performed after calculating sentiment for individual sentences.
Why you need to analyse the sentiment
Sentiment analysis is critical because emotions and attitudes are useful information in many business and research areas. As customer service becomes more automated through machine learning, it becomes more important to understand the emotions and intentions of a particular case.
Getting feedback and analysing sentiment can give companies a powerful boost to how customers really “think” of their brand. Whether you’re using a text analytics tool for survey responses or a social media management tool for data mining, keeping a close eye on customer reviews is important. Once sentiment has been assessed in your survey responses, you can address some of the most pressing issues your customers have during their experience. Customer opinions can be found in tweets, comments, reviews, or other places where people mention your brand.
Social media sentiment is the attitude and feelings of people towards your brand on social media. Overall mood is often defined as positive, neutral, or negative based on the sign of polarity. By applying this contextual understanding to a sentence, we can easily define this feeling as negative. When it comes to irony and sarcasm, people express their negative feelings with positive words that can be difficult for machines to detect without fully understanding the context of the situation in which the sentiment was expressed.
The subjective text contains text that is usually expressed by people with typical emotions, emotions, and feelings. When using natural language processing to analyse a piece of unstructured text, each concept in the specified environment will be scored based on the relationship between the emotional word and the concept and the related score.
For example, words that reinforce, relax, or negate the feeling expressed by the concept will affect its judgment.
Kommon Poll allows users to track online customer sentiment in social media and traditional media and analyses the sentiment of each of these mentions. Using complex filters, you can also segment sentiment by sources and get an overall sentiment score for your keyword. It doesn’t end there! We also provide Named Entity Recognition, and other mention analysis features to identify relationships between various mentions better.
Try out Kommon Poll now with a free trial!
Citations
- https://brand24.com/blog/sentiment-analysis/ 0
- https://www.tidytextmining.com/sentiment.html 1
- https://www.kdnuggets.com/2018/08/emotion-sentiment-analysis-practitioners-guide-nlp-5.html 2
- https://callminer.com/blog/sentiment-analysis-examples-best-practices 3
- https://towardsdatascience.com/sentiment-analysis-concept-analysis-and-applications-6c94d6f58c17 4
- https://searchbusinessanalytics.techtarget.com/definition/opinion-mining-sentiment-mining 5
- https://cnvrg.io/sentiment-analysis-python/ 6
- https://en.wikipedia.org/wiki/Sentiment_analysis 7
- https://www.qualtrics.com/experience-management/research/sentiment-analysis/ 8
- https://algorithmia.com/blog/introduction-sentiment-analysis 9
- https://www.brandwatch.com/blog/understanding-sentiment-analysis/ 10
- https://journalofbigdata.springeropen.com/articles/10.1186/s40537-015-0015-2 11
- https://blog.hootsuite.com/social-media-sentiment-analysis-tools/ 12
- https://monkeylearn.com/sentiment-analysis/ 13
- https://www.lexalytics.com/technology/sentiment-analysis 14
- https://getthematic.com/insights/sentiment-analysis/ 15