Sentiment Analysis

Sentiment Analysis: Opinion mining through Emotion recognition

Sentiment analysis, also known as opinion mining, is the process of using natural language processing and machine learning techniques to determine the emotional tone behind words. It is a powerful tool used to identify and extract subjective information from source materials, and is commonly used to discover how people feel about a particular product, service, or topic.

Sentiment analysis is a powerful tool for understanding the emotions behind text data. By gathering relevant data, cleaning the text, and using various methods for analysis, sentiment analysis can provide valuable insights into how people feel about a particular product, service, or topic.

Data Gathering

The first step in sentiment analysis is gathering data. This data can come from various sources such as social media posts, customer reviews, and survey responses. It is important to gather a large and diverse dataset to ensure accurate analysis. The data should also be relevant to the topic being studied. For example, if the sentiment analysis is being performed on a new product, it would be appropriate to gather data from customer reviews on that specific product.

Text Cleaning

Once the data is gathered, the next step is to clean the text. This process involves removing any irrelevant information such as special characters, numbers, and stop words. Stop words are common words such as “the,” “and,” and “is” that do not add any meaning to the text. Text cleaning also involves converting all text to lowercase to ensure consistency. This step is crucial as it helps to improve the accuracy of the analysis.

Analyzing the Data

After the text has been cleaned, the data is ready for analysis. There are several methods for performing sentiment analysis, including lexicon-based, rule-based, and machine learning-based.

Lexicon-based sentiment analysis involves using a pre-defined list of words and their associated sentiment to analyze text. For example, words such as “happy” and “exciting” are associated with positive sentiment, while words such as “sad” and “angry” are associated with negative sentiment.

Rule-based sentiment analysis involves using a set of predefined rules to analyze text. For example, a rule could be established that a sentence containing the word “disappointed” has a negative sentiment.

Machine learning-based sentiment analysis involves training a model to understand the sentiment behind text. This method is considered to be the most accurate as it allows the model to learn and adapt over time.

Understanding the results from sentiment analysis

Understanding the results from sentiment analysis involves analyzing the output of the analysis in relation to the original data.

One common output is a sentiment score, which is a numerical representation of the sentiment in the text. This score can be a positive or negative number, with positive numbers indicating a positive sentiment and negative numbers indicating a negative sentiment. The magnitude of the number can indicate the strength of the sentiment. For example, a score of -0.8 would indicate a stronger negative sentiment than a score of -0.2.

Another common output is a sentiment classification, which categorizes the text as positive, negative, or neutral. The classification is usually based on a threshold. For example, a score above 0 is positive, a score below 0 is negative and a score around 0 is neutral.

It’s important to keep in mind that the results of sentiment analysis should be interpreted in the context of the data and the specific use case. If the data is a collection of customer reviews, for example, a high percentage of negative sentiment could indicate that customers are unhappy with the product. However, if the data is a collection of tweets about a political candidate, a high percentage of negative sentiment could indicate that the candidate is controversial.

Additionally, it is also important to validate the results by checking the accuracy of the model using a labeled dataset, this can be done by using techniques like cross-validation, confusion matrix and other metrics to evaluate the performance of the model.

In summary, understanding the results from sentiment analysis involves analyzing the output in relation to the original data, interpreting it in the context of the use case, and validating the results with metrics.

If you have a product which you wish to evaluate by using sentiment analysis, please drop in a line at info@rewiseglobal.com, we’d be happy to help to get started with this powerful tool.