How do you handle sentiment shifters in NLP models?

1 Types of sentiment shifters

Sentiment shifters can be divided into three main types: negators, intensifiers, and diminishers. Negators are words that negate or reverse the sentiment of a word or phrase, such as not, never, or none. For example, "This movie is good" has a positive sentiment, but "This movie is not good" has a negative sentiment. Intensifiers are words that amplify or strengthen the sentiment of a word or phrase, such as very, extremely, or absolutely. For example, "This movie is good" has a positive sentiment, but "This movie is very good" has a more positive sentiment. Diminishers are words that weaken or soften the sentiment of a word or phrase, such as slightly, somewhat, or barely. For example, "This movie is good" has a positive sentiment, but "This movie is slightly good" has a less positive sentiment.

2 Challenges of sentiment shifters

Sentiment shifters pose several challenges for NLP models that perform sentiment analysis. First, they can modify the sentiment of a word or phrase in different ways depending on the context and the domain. For example, the word "only" can be a diminisher, as in "He only scored two goals", or an intensifier, as in "He scored two goals only". Second, they can have a scope that affects more than one word or phrase in a sentence. For example, the word "not" can negate the sentiment of the whole sentence, as in "Not a single scene was interesting", or only part of it, as in "The movie was not boring, but predictable". Third, they can interact with each other and create complex patterns of sentiment modification. For example, the phrase "not very good" has a negative sentiment, but the phrase "not very bad" has a positive sentiment.

3 Solutions for sentiment shifters

To effectively handle sentiment shifters in your NLP models, you need to use techniques that can accurately capture their effects and interactions at different levels of granularity. Syntactic features or parsers can be used to identify the structure and scope of sentiment shifters in a sentence, such as by using dependency parsing to find the relations between words and phrases and their modifiers. Additionally, semantic features or embeddings can be used to represent the meaning and polarity of words and phrases, while attention mechanisms or transformers can measure how much each word or phrase contributes to the overall sentiment. All of these techniques can then be used as inputs for your sentiment classifier.

4 Evaluation of sentiment shifters

To evaluate how well your NLP models handle sentiment shifters, you need to use datasets that contain sentences with different types and combinations of sentiment shifters, as well as metrics that measure how accurately your models capture the polarity and intensity of the sentences. For example, the Stanford Sentiment Treebank (SST) is a dataset that annotates each word and phrase in a sentence with a fine-grained sentiment label. The MPQA Opinion Corpus is a dataset that annotates each word and phrase in a sentence with a subjective or objective label, and a positive or negative polarity label. The Sentiment Intensity Analysis (SIA) is a metric that measures how well your models estimate the intensity of the sentiment in a sentence, using a scale from -4 (extremely negative) to 4 (extremely positive). All of these can help test how well your models detect sentiment shifters and their scope, distinguish between subjective and objective expressions, and account for the effects and interactions of the sentiment shifters in a sentence.

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?