sentiment analysis (opinion mining)

What is sentiment analysis (opinion mining)?

Sentiment analysis, also referred to as opinion mining, is an approach to natural language processing (NLP) that identifies the emotional tone behind a body of text. This is a popular way for organizations to determine and categorize opinions about a product, service, or idea. It involves the use of data mining, machine learning (ML) and artificial intelligence (AI) to mine text for sentiment and subjective information.

Sentiment analysis systems help organizations gather insights from unorganized and unstructured text that comes from online sources such as emails, blog posts, support tickets, web chats, social media channels, forums and comments. Algorithms replace manual data processing by implementing rule-based, automatic or hybrid methods. Rule-based systems perform sentiment analysis based on predefined, lexicon-based rules while automatic systems learn from data with machine learning techniques. A hybrid sentiment analysis combines both approaches.

In addition to identifying sentiment, opinion mining can extract the polarity (or the amount of positivity and negativity), subject and opinion holder within the text. Furthermore, sentiment analysis can be applied to varying scopes such as document, paragraph, sentence and sub-sentence levels.

Vendors that offer sentiment analysis platforms or SaaS products include Brandwatch, Hootsuite, Lexalytics, NetBase, Sprout Social, Sysomos and Zoho. Businesses that use these tools can review customer feedback more regularly and proactively respond to changes of opinion within the market.

Types of sentiment analysis

Types of sentiment analysis

  1. Fine-grained sentiment analysis provides a more precise level of polarity by breaking it down into further categories, usually very positive to very negative. This can be considered the opinion equivalent of ratings on a 5-star scale.
  2. Emotion detection identifies specific emotions rather than positivity and negativity. Examples could include happiness, frustration, shock, anger and sadness.
  3. Intent-based analysis recognizes actions behind a text in addition to opinion. For example, an online comment expressing frustration about changing a battery could prompt customer service to reach out to resolve that specific issue.
  4. Aspect-based analysis gathers the specific component being positively or negatively mentioned. For example, a customer might leave a review on a product saying the battery life was too short. Then, the system will return that the negative sentiment is not about the product as a whole, but about the battery life.

Applications of sentiment analysis

Sentiment analysis tools can be used by organizations for a variety of applications, including:

  • Identifying brand awareness, reputation and popularity at a specific moment or over time.
  • Tracking consumer reception of new products or features.
  • Evaluating the success of a marketing campaign.
  • Pinpointing the target audience or demographics.
  • Collecting customer feedback from social media, websites or online forms.
  • Conducting market research.
  • Categorizing customer service requests.

Challenges with sentiment analysis

Challenges associated with sentiment analysis typically revolve around inaccuracies in training models. Objectivity, or comments with a neutral sentiment, tend to pose a problem for systems and are often misidentified. For example, if a customer received the wrong color item and submitted a comment "The product was blue," this would be identified as neutral when in fact it should be negative.

Sentiment can also be challenging to identify when systems cannot understand the context or tone. Answers to polls or survey questions like "nothing" or "everything" are hard to categorize when the context is not given, as they could be labeled as positive or negative depending on the question. Similarly, irony and sarcasm often cannot be explicitly trained and lead to falsely labeled sentiments.

Computer programs also have trouble when encountering emojis and irrelevant information. Special attention needs to be given to training models with emojis and neutral data so as to not improperly flag texts.

Finally, people can be contradictory in their statements. Most reviews will have both positive and negative comments, which is somewhat manageable by analyzing sentences one at a time. However, the more informal the medium, the more likely people are to combine different opinions in the same sentence and the more difficult it will be for a computer to parse.

This was last updated in October 2021

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