Use Gin framework to implement text analysis and sentiment analysis functions

In recent years, with the popularity of social media and the development of mobile Internet, the number of articles and comments shared and published by people on online platforms has exploded. These texts not only cover various topics, but also contain rich content. Emotional color.

It is very important for companies and individuals to understand the public's attitudes and emotions towards their brands, products and services. Therefore, there is an increasing need to implement text analysis and sentiment analysis capabilities. In this article, we will introduce how to use the Gin framework to implement text analysis and sentiment analysis functions.

1. Introduction to Gin framework

The Gin framework is a Web framework written in Go language. It implements high-performance API services by using efficient memory reuse. Gin is designed based on the ideas of the Martini framework, but it has better performance and better APIs and can be used to build small and medium-sized web applications. It is also very suitable for building RESTful API services.

2. Install the Gin framework

Before we start, we need to install the Gin framework and related dependent libraries. Before installation, you need to install the Golang development environment. Enter the following command in your terminal to install the Gin framework:

go get -u github.com/gin-gonic/gin

In addition, we also need to install the following two dependent libraries:

go get -u gopkg.in/yaml.v2
go get -u github.com/cdipaolo/sentiment

3. Implement text analysis function

Before implementing sentiment analysis, we need to implement some basic text analysis functions.

1. Word segmentation

For a piece of text, we need to break it down into individual words. This process is called word segmentation. In the Go language, we can use the third-party library github.com/blevesearch/go-porterstemmer to implement this function. The following is a simple code example:

import (
    "github.com/blevesearch/go-porterstemmer"
    "strings"
)

func Tokenize(text string) []string {
    // Remove unnecessary characters
    text = strings.ReplaceAll(text, ".", "")
    text = strings.ReplaceAll(text, ",", "")
    text = strings.ReplaceAll(text, "!", "")
    text = strings.ReplaceAll(text, "?", "")
    text = strings.ToLower(text)

    // Split text into words
    words := strings.Fields(text)

    // Stem words using Porter Stemmer algorithm
    for i, w := range words {
        words[i] = porterstemmer.Stem(w)
    }

    return words
}

2. Count word frequency

After word segmentation, we need to count the number of times each word appears in the text. This process is called statistics Word frequency. The following is a simple code example:

func CalculateTermFrequency(words []string) map[string]int {
    frequency := make(map[string]int)

    for _, w := range words {
        _, exists := frequency[w]
        if exists {
            frequency[w]++
        } else {
            frequency[w] = 1
        }
    }

    return frequency
}

4. Implementing the sentiment analysis function

Before implementing the sentiment analysis function, we need to establish an emotional lexicon to store emotional words. Words and their sentiment weight. Here, we use the sentiment dictionary file AFINN-165.txt. The following is part of the file:

abandons    -2
abducted    -2
abduction    -2
abductions    -2
abhor    -3
abhorred    -3
abhorrent    -3
abhorring    -3
abhors    -3
abilities    2
...

We can use the following code to read the sentiment dictionary file and store it into a map:

import (
    "bufio"
    "os"
    "strconv"
    "strings"
)

func LoadSentimentWords(filename string) (map[string]int, error) {
    f, err := os.Open(filename)
    if err != nil {
        return nil, err
    }
    defer f.Close()

    sentiments := make(map[string]int)

    scanner := bufio.NewScanner(f)
    for scanner.Scan() {
        splitted := strings.Split(scanner.Text(), "    ")
        word := splitted[0]
        value, err := strconv.Atoi(splitted[1])
        if err != nil {
            continue
        }
        sentiments[word] = value
    }

    return sentiments, nil
}

After reading the sentiment dictionary file, We can use the following code to calculate the sentiment score of a text:

import (
    "github.com/cdipaolo/sentiment"
    "github.com/ryangxx/go-sentiment-analysis/text"
)

func CalculateSentimentScore(text string, sentiments map[string]int) (float64, error) {
    words := text.Tokenize(text)
    wordCount := len(words)

    score := 0
    for _, w := range words {
        value, exists := sentiments[w]
        if exists {
            score += value
        }
    }

    return float64(score) / float64(wordCount), nil
}

The above code uses the third-party library github.com/cdipaolo/sentiment to perform sentiment analysis. This library is a Go language implementation of the NLTK-based Python library VADER, which can directly calculate the sentiment score of a text.

5. Building API services

We have successfully implemented text analysis and sentiment analysis functions. Now, we need to integrate these functions into a RESTful API service.

The following is our directory structure:

- main.go
- config/
  - config.yaml
- internal/
  - analyzer/
    - analyzer.go
  - handler/
    - handler.go
  - model/
    - sentiment.go

The config/config.yaml file is used to store configuration information, such as the file path of the emotional vocabulary library. The following is a sample configuration file:

analyzer:
  sentimentFile: "data/AFINN-165.txt"
  tokenizing:
    remove:
      - "."
      - ","
      - "!"
      - "?"
    toLowercase: true

analyzer/analyzer.go file is our main analysis program. It contains all functions for word segmentation and sentiment calculation. The handler/handler.go file contains our API handler. Finally, we defined a Sentiment structure in the model/sentiment.go file as the return type of the API response.

The following is the main code:

package main

import (
    "github.com/gin-gonic/gin"
    "github.com/ryangxx/go-sentiment-analysis/analyzer"
    "github.com/ryangxx/go-sentiment-analysis/handler"
)

func main() {
    router := gin.Default()

    sentimentAnalyzer := analyzer.NewSentimentAnalyzer()
    sentimentHandler := handler.NewSentimentHandler(sentimentAnalyzer)

    router.GET("/analysis", sentimentHandler.GetSentimentAnalysis)

    router.Run(":8080")
}

6. API Test

Now, we have completed our API service. We can use curl command or postman to test it.

The following is an example of a curl command:

curl --location --request GET 'http://localhost:8080/analysis?text=I%20love%20Golang'

This API will return a JSON object:

{
    "message": "OK",
    "sentiment": {
        "score": 0.6
    }
}

In this JSON object, score is the sentiment score. Its value ranges from -1 to 1, where -1 is completely negative, 0 is neutral, and 1 is completely positive.

7. Conclusion

In this article, we introduced how to use the Gin framework to build API services for text analysis and sentiment analysis. We developed a sentiment analyzer using the Go language, which can read a sentiment vocabulary and calculate the sentiment score of a text. We also show how to build this sentiment analyzer into a RESTful API service using the Gin framework.

It is worth pointing out that although we are using the AFINN-165.txt sentiment dictionary in this article, this is not the only option. In the real world, there are multiple sentiment dictionaries to choose from, each of which has its advantages and disadvantages. Therefore, in practical applications, we need to choose the sentiment dictionary that best suits our needs.

In general, the text analysis and sentiment analysis API services built on the Gin framework are very effective and practical and can help us better understand the public's attitudes and emotions towards our brands, products and services.

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