LLMs for Sentiment Analysis

Sentiment analysis, or opinion mining, is the process of determining the sentiment, emotion, or attitude expressed in a piece of text. Sentiment Analysis is used for various applications, including customer feedback analysis, brand monitoring, political analysis, market research and financial analysis.

Large language models, such as GPT-4, can perform sentiment analysis by leveraging their extensive training on vast amounts of text data, which enables them to recognise and understand patterns in language use.

In the context of large language models, this involves a few key steps:

Tokenisation

Tokenisation involves breaking down the input text into smaller, meaningful units called tokens. These tokens can be individual words, phrases, or sentences, depending on the level of granularity required for the task at hand.

Tokenisation is important because it helps to standardise the text and prepare it for further processing by the model. By breaking the text down into smaller units, the model can better understand the structure and syntax of the language and identify relevant features that are useful for sentiment analysis.

There are several ways to tokenise text, including:

• Word-level tokenisation: This involves breaking the text into individual words, ignoring punctuation or other non-word characters. For example, the sentence “I love pizza!” would be tokenised into the following words: [“I”, “love”, “pizza”].
• Phrase-level tokenisation: This involves breaking the text down into meaningful phrases or chunks of words. This can be useful for capturing multi-word expressions commonly associated with certain sentiments, such as “heartbroken” or “over the moon”. For example, the phrase “over the moon” would be tokenised as a single phrase token.
• Sentence-level tokenisation: This involves breaking the text down into individual sentences, which can be useful for analysing sentiment at a more granular level. For example, a product review may contain multiple sentences, each expressing a different aspect of the user’s experience.

In practice, a combination of these tokenisation methods may be used to achieve the desired level of granularity for sentiment analysis.

Text preprocessing

After tokenisation, the next step in sentiment analysis is typically text preprocessing. This involves cleaning and normalising the text to remove any irrelevant information or noise that might interfere with the model’s ability to accurately classify sentiment.

Ccommon text preprocessing steps include:

1. Removing punctuation: Punctuation marks, such as periods, commas, and exclamation points, are typically not relevant for sentiment analysis and can be removed to simplify the text.
2. Lowercasing: Converting all text to lowercase can help to standardise the text and reduce the number of unique tokens that the model needs to process.
3. Removing stop words: Stop words are common words that do not carry much meaning on their own, such as “the”, “a”, and “an”. These words can be removed to reduce noise in the text and improve the efficiency of the model.
4. Stemming or lemmatisation: Stemming and lemmatisation are techniques for reducing words to their root form, which can help to reduce the number of unique tokens that the model needs to process. For example, the words “running”, “runner”, and “runners” could all be stemmed to the root form “run”.
5. Removing special characters: Special characters, such as emojis or emoticons, may not be relevant for sentiment analysis and can be removed to simplify the text.

The specific preprocessing steps used may vary depending on the type of text data being analysed and the goals of the sentiment analysis task.

Feature extraction

The next step in sentiment analysis is feature extraction, where the model identifies relevant features in the preprocessed text that are indicative of sentiment.

The specific features used may depend on the particular sentiment analysis task and the domain of the text being analysed, but some common features used in sentiment analysis include:

• Bag-of-words: A bag-of-words representation involves counting the frequency of each word in the text and using these counts as features for sentiment classification. This approach assumes that the presence or absence of particular words is indicative of sentiment, and can be useful for identifying keywords associated with positive or negative sentiment.
• N-grams: An N-gram is a sequence of N adjacent words in the text, where N can be any positive integer. By counting the frequency of different N-grams in the text, the model can identify patterns of word combinations that are indicative of sentiment. For example, the phrase “not happy” might be represented as a 2-gram (“not happy”), which could be indicative of negative sentiment.
• Part-of-speech (POS) tags: POS tags indicate the grammatical role of each word in the text, such as whether it is a noun, verb, or adjective. By considering the distribution of different POS tags in the text, the model can identify patterns of language use that are associated with sentiment.
• Sentiment lexicons: Sentiment lexicons are collections of words or phrases that are manually annotated with sentiment labels (e.g., positive, negative, or neutral). By comparing the words in the input text to the words in the sentiment lexicon, the model can identify words that are likely to be associated with particular sentiment categories.

Once the relevant features have been extracted, the model can use them to classify the sentiment of the input text. This may involve training a machine learning algorithm to learn patterns of feature use that are indicative of sentiment, or using a rule-based approach that assigns sentiment based on the presence of particular features.

Classification

The next step in sentiment analysis is sentiment classification, where the model assigns a sentiment label to the input text based on the relevant features that have been extracted. There are several ways to perform sentiment classification, including:

• Rule-based approaches: Rule-based approaches involve defining a set of rules or heuristics that the model uses to classify sentiment based on the presence or absence of particular features. For example, a rule-based approach might assign positive sentiment if the input text contains words like “happy”, “excited”, or “delighted”, and negative sentiment if the input text contains words like “angry”, “disappointed”, or “frustrated”.
• Machine learning approaches: Machine learning approaches involve training a machine learning algorithm to learn patterns of feature use that are indicative of sentiment. This typically involves using a labelled dataset of text data, where each piece of text is labelled with a sentiment category (e.g., positive, negative, or neutral). The machine learning algorithm then learns to recognise patterns in the text data that are associated with each sentiment category and can use these patterns to classify new text.
• Hybrid approaches: Hybrid approaches combine both rule-based and machine learning techniques to improve the accuracy of sentiment classification. For example, a hybrid approach might use a rule-based approach to assign sentiment based on the presence of particular keywords but use a machine learning algorithm to refine the classification based on additional features like sentiment lexicons or part-of-speech tags.

The specific approach used for sentiment classification may depend on the nature of the text data being analysed, the goals of the sentiment analysis task, and the resources available for model training and development.

Interpretation

The final step in sentiment analysis is interpretation, where the model’s output is interpreted and used to inform further analysis or decision-making.

The interpretation step may involve several tasks, including:

• Aggregation: Sentiment analysis is often performed on large volumes of text data, such as social media posts or customer reviews. In these cases, the output of the sentiment analysis model may need to be aggregated across multiple pieces of text to provide an overall measure of sentiment for a particular topic, product, or brand.
• Visualisation: Visualisation techniques can be used to display the sentiment analysis results in an easily understandable way, such as through a pie chart or a bar graph. This can help to highlight the distribution of sentiment across different categories or topics.
• Decision-making: The output of the sentiment analysis model can be used to inform decision-making processes, such as product development or customer service strategies. For example, a company might use sentiment analysis to identify common complaints or issues among its customers and use this information to improve its products or services.
• Evaluation: Finally, the output of the sentiment analysis model should be evaluated to ensure that it is accurate and reliable. This may involve comparing the model’s output to human annotations or other sources of ground truth data, and measuring metrics like precision, recall, and F1 score to assess the model’s performance.

Large language models like GPT-4 are particularly well-suited for sentiment analysis due to their ability to understand context, sarcasm, and nuanced expressions in text. They have been pre-trained on diverse sources of text data, which enables them to recognise subtle patterns and relationships between words, phrases, and emotions, ultimately leading to more accurate sentiment analysis.