forked from SocialHarvest/sentiment
/
sentiment.go
124 lines (104 loc) · 2.83 KB
/
sentiment.go
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// Copyright 2015 Tom Maiaroto, Shift8Creative
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
package sentiment
import (
"github.com/jbrukh/bayesian"
"io"
"log"
"net/http"
"os"
"strings"
)
const (
Positive bayesian.Class = "Positive"
Negative bayesian.Class = "Negative"
Neutral bayesian.Class = "Neutral"
)
const DATA_FILE = "./sentiment-data/sentiment-classifier.dmp"
type Analyzer struct {
classifier *bayesian.Classifier
}
// Classifies a string
func (a *Analyzer) Classify(s string) int {
if len(s) <= 2 {
return 0
}
tokens := tokenize(s)
_, likely, _ := a.classifier.LogScores(tokens)
sentiment := 0
// Positive, Negative, Neutral was the order in which classes were defined
switch likely {
case 0:
sentiment = 1
case 1:
sentiment = -1
case 2:
sentiment = 0
}
return sentiment
}
// Sets up and trains a new analyzer to classify sentiment
func NewAnalyzer() Analyzer {
a := Analyzer{}
// Get the training data if not present
_, err := os.Stat(DATA_FILE)
if err != nil {
if os.IsNotExist(err) {
a.downloadDataSet()
}
}
c, err := bayesian.NewClassifierFromFile(DATA_FILE)
if err == nil {
a.classifier = c
} else {
// Note: Nothing will be trained at this point, but we'll still have a classifier that can be trained
a.classifier = bayesian.NewClassifier(Positive, Negative, Neutral)
}
return a
}
// Retrieves training data (which is much too large to keep in GitHub)
func (a *Analyzer) downloadDataSet() {
os.Mkdir("./sentiment-data", 0777)
out, oErr := os.Create(DATA_FILE)
defer out.Close()
if oErr == nil {
r, rErr := http.Get("https://s3.amazonaws.com/socialharvest/public-data/sentiment/sentiment-classifier.dmp")
defer r.Body.Close()
if rErr == nil {
_, nErr := io.Copy(out, r.Body)
if nErr != nil {
err := os.Remove(DATA_FILE)
if err != nil {
log.Println(err)
}
}
r.Body.Close()
} else {
log.Println(rErr)
}
out.Close()
} else {
log.Println(oErr)
}
}
// Splits apart a string to train/classify it by word and word n-grams.
func tokenize(s string) []string {
tokens := []string{}
tokenSlice := strings.Split(s, " ")
for k, v := range tokenSlice {
tokens = append(tokens, v)
if len(tokenSlice)-1 > k && len(v) > 1 {
ngram := tokenSlice[k] + " " + tokenSlice[k+1] //+ " " + tokenSlice[k+2]
tokens = append(tokens, ngram)
}
}
return tokens
}