What are some best practices for adapting a general sentiment lexicon to a domain-specific one?

1 Choose a general lexicon

The first step is to choose a general sentiment lexicon that suits your needs and goals. There are many existing sentiment lexicons that you can use or modify, such as SentiWordNet, VADER, or LIWC. You should consider factors such as the size, coverage, granularity, and format of the lexicon, as well as the language and the source of the data used to create it. You should also evaluate the quality and validity of the lexicon by comparing it with human annotations or other benchmarks.

2 Identify domain-specific terms

The next step is to identify the terms that are specific to your domain and that may have different sentiment values than in the general lexicon. For example, if you are analyzing movie reviews, you may want to include terms such as "plot", "acting", "cinematography", or "genre" in your lexicon. You can use various methods to find these terms, such as keyword extraction, term frequency analysis, or topic modeling. You should also look for domain-specific modifiers, such as intensifiers, negators, or contrastive markers, that can affect the sentiment polarity or strength of a term.

3 Assign sentiment scores

The third step is to assign sentiment scores to the domain-specific terms that you have identified. You can use different approaches to do this, such as manual annotation, rule-based methods, or machine learning models. Manual annotation involves asking human experts or crowdsourced workers to rate the sentiment of each term on a scale or a category. Rule-based methods involve applying predefined rules or heuristics to infer the sentiment of a term based on its syntactic or semantic features. Machine learning models involve training a supervised or unsupervised algorithm to learn the sentiment of a term from labeled or unlabeled data.

4 Validate and update your lexicon

The final step is to validate and update your lexicon to ensure its accuracy and relevance. You should test your lexicon on a sample of domain-specific texts and measure its performance using metrics such as precision, recall, or F1-score. You should also compare your lexicon with other lexicons or methods and analyze the sources of errors or discrepancies. You should also monitor the changes and trends in your domain and update your lexicon accordingly. For example, you may need to add new terms, remove outdated terms, or adjust sentiment scores based on new data or feedback.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?