Conducting Sentiment Analysis

Sentiment analysis is the process of analyzing text to determine the emotional tone or sentiment expressed in it. It is commonly used in various domains such as social media, customer feedback, and news analysis. There are two main methods of sentiment analysis: supervised machine learning and unsupervised learning. This Element provides an introduction to sentiment analysis, including its definition, domains, methods, and examples.

Format: Paperback / softback
Length: 75 pages
Publication date: 23 September 2021
Publisher: Cambridge University Press

Sentiment analysis is a field of study that involves the analysis of written or spoken language to determine the emotional tone or sentiment expressed in the content. It is a crucial aspect of natural language processing (NLP) and has numerous applications in various domains such as social media analysis, customer feedback analysis, and news analysis.

Sentiment analysis is a complex process that involves several steps. Firstly, the text or speech data is preprocessed to remove any unnecessary noise, such as punctuation marks, stopwords, and abbreviations. This step helps to clean the data and make it easier for the algorithm to analyze.

Once the data is preprocessed, it is fed into a sentiment analysis algorithm, which uses machine learning techniques to classify the emotional tone of the content. The algorithm analyzes the text based on patterns and features such as word choice, sentence structure, and lexical resources.

There are two main methods of sentiment analysis: supervised machine learning and unsupervised learning (or lexicon-based) methods. Supervised machine learning involves training a model on a labeled dataset, where the emotional tone of the content is explicitly labeled. The model uses this labeled data to learn the patterns and features that are associated with different emotional tones and then applies these learned patterns to classify new text.

Unsupervised learning, on the other hand, involves analyzing unlabeled data and identifying patterns and features that are associated with different emotional tones. The algorithm uses this unlabeled data to cluster the text into different groups based on its emotional tone.

Once the sentiment analysis algorithm has classified the text, it produces a sentiment score, which ranges from negative to positive. A positive sentiment score indicates that the content expresses a positive emotional tone, while a negative sentiment score indicates that the content expresses a negative emotional tone.

There are numerous applications of sentiment analysis in various domains. For example, in social media analysis, sentiment analysis can be used to analyze user comments and feedback to understand the sentiment of customers towards a particular product or service. In customer feedback analysis, sentiment analysis can be used to analyze customer reviews and feedback to identify areas for improvement and to measure customer satisfaction. In news analysis, sentiment analysis can be used to analyze news articles and news headlines to identify the emotional tone of the content and to track the sentiment of public opinion over time.

In conclusion, sentiment analysis is a field of study that involves the analysis of written or spoken language to determine the emotional tone or sentiment expressed in the content. It is a crucial aspect of natural language processing (NLP) and has numerous applications in various domains such as social media analysis, customer feedback analysis, and news analysis. There are two main methods of sentiment analysis: supervised machine learning and unsupervised learning (or lexicon-based) methods. Supervised machine learning involves training a model on a labeled dataset, while unsupervised learning involves analyzing unlabeled data and identifying patterns and features that are associated with different emotional tones.

Weight: 178g
Dimension: 230 x 229 x 9 (mm)
ISBN-13: 9781108829212