Skip to content

Latest commit

 

History

History
65 lines (57 loc) · 3.04 KB

texts-sentiment.md

File metadata and controls

65 lines (57 loc) · 3.04 KB
Raw

Texts Sentiment Analysis

This method aims to provide a support for the analysis of the Sentiment a given list of texts.

Usage

The usage is really simple

import { textSentiment } from '@matteocacciola/sentiment'

const results: SentimentResult = await textSentiment(texts);
const results: SentimentResult = await textSentiment(texts, options);

where texts is an array of texts you want to analyze the Sentiment.

Options

The options has the format { scoresEvaluator: ScoresEvaluator; scanPeriodDays: number; scoreThreshold: number; scoresEvaluatorOptions: ScoresEvaluatorOptions }, where:

  • scoresEvaluator (default 'afinn') identifies the strategy to use for the evaluation of the scores and sentiments of the various collected data with type ScoresEvaluatorStrategy = 'afinn' | 'google' | 'vader' | 'bayes' (AFINN; Google Natural Language; VADER; Naive Bayes - default 'afinn'). You can also pass a custom evaluator implementing the following type: 
    type EvaluateScoresFunction = (
  items: string[],
  scoreThreshold: number,
  scoresEvaluatorOptions?: ScoreStrategyOptions
) => Promise<ScoresEvaluatorResult[]>;
  • scoreThreshold (default 0.3) identifies the "sentiment category" of a specific text. A number greater than 1 is automatically scaled to one. Loosely speaking, each "sentiment score" can range within the interval [-1, 1]:
    • when the score is within [-1, -scoreThreshold], it is considered negative
    • when the score is within ]-scoreThreshold, scoreThreshold[, it is labelled neutral
    • when the score is within [scoreThreshold, 1], it is positive
    • finally, when no score can be retrieved, then the assigned category is undefined
  • scoresEvaluatorOptions: if scoresEvaluator is 'google', you can set some options according to the Google Cloud Natural Language API; if scoresEvaluator is 'bayes', you can set some preferences according to the documentation of the Naive Bayes library.

Result

It has the format:

type SentimentResult = {
  positive: number;
  negative: number;
  neutral: number;
  analyzedElements: Score[];
  sentimentScore: number;
  analyzedAt: string;
};

type Score = {
  text: string;
  score?: number;
  category?: SentimentType;
};

type SentimentType = 'positive' | 'negative' | 'neutral' | 'undefined';

Notes

The available scoresEvaluator allows to use evaluations of Sentiment and scores by means of predefined strategies, already implemented within this package, or by using a custom and self-implemented Sentiment classifier, e.g. obtained by training suitable Machine Learning structures. If you want to consult possible algorithms you can train, please feel free to be inspired by my Machine Learning codes like Support Vector Machines, Convolutional Neural Network, Multilayer Perceptron Artificial Neural Network, Fuzzy C-means and so forth.