我有一个文本blob，如果极性为＆gt;我将文本分类为正数。 0，中性if = 0，如果＆lt; 0 0。 我怎样才能得到正在分类为积极，消极或中立的词语？
答案 0 :(得分：1)
我希望以下代码能为您提供帮助：
from textblob import TextBlob
from textblob.sentiments import NaiveBayesAnalyzer
import nltk
nltk.download('movie_reviews')
nltk.download('punkt')
text = "I feel the product is so good"
sent = TextBlob(text)
# The polarity score is a float within the range [-1.0, 1.0]
# where negative value indicates negative text and positive
# value indicates that the given text is positive.
polarity = sent.sentiment.polarity
# The subjectivity is a float within the range [0.0, 1.0] where
# 0.0 is very objective and 1.0 is very subjective.
subjectivity = sent.sentiment.subjectivity
sent = TextBlob(text, analyzer = NaiveBayesAnalyzer())
classification= sent.sentiment.classification
positive = sent.sentiment.p_pos
negative = sent.sentiment.p_neg
print(polarity,subjectivity,classification,positive,negative)
答案 1 :(得分：0)
一定要给维德一个机会。 Vader 是一种基于规则的情感分析工具，适用于社交媒体文本和常规文本。
import SwiftUI
import MapKit
struct ContentView: View {
@State private var centerCoordinates = CLLocationCoordinate2D()
@State private var showingConfirmation = false
@State private var showingAlert = false
var body: some View {
VStack {
GeometryReader { geo in
ZStack {
MapView(centerCoordinate: $centerCoordinates)
.edgesIgnoringSafeArea(.all)
.blur(radius: showingConfirmation ? 6.0 : 0)
Circle()
.fill(Color.blue)
.opacity(0.3)
.frame(width: 32, height: 32)
.blur(radius: showingConfirmation ? 6.0 : 0)
Text("Get Coordinates").bold()
.foregroundColor(.primary)
.padding()
.background(Color.blue)
.clipShape(Capsule())
.blur(radius: showingConfirmation ? 6.0 : 0)
.offset(x: 0, y: geo.size.height * 0.45)
.onTapGesture {
print(centerCoordinates)
showingAlert = true
}.disabled(showingConfirmation)
if showingConfirmation {
Rectangle()
.fill(Color.clear)
.frame(height: 500)
.overlay(
VStack {
Text("Latitude and Longitude copied!")
.font(.title)
.bold()
.lineLimit(3)
Button(action: {self.showingConfirmation = false}){
Text("Ok")
.fontWeight(.bold)
.padding()
.foregroundColor(.white)
.background(Color.blue)
.cornerRadius(10)
}
}
)
}
}
}
}
.actionSheet(isPresented: $showingAlert) {
ActionSheet(title: Text("Your coordinates are: \nLatitude: \(centerCoordinates.latitude) \nLongitude: \(centerCoordinates.longitude)"), message: nil, buttons: [
.default(Text("Ok")),
.default(Text("Copy Latitude and Longitude")) {
let myCoordinates = [String(centerCoordinates.latitude), String(centerCoordinates.longitude)]
let pasteboard = UIPasteboard.general
pasteboard.strings = myCoordinates
showingConfirmation = true
}
]
)
}
}
}
struct ContentView_Previews: PreviewProvider {
static var previews: some View {
ContentView()
.preferredColorScheme(.dark)
}
}
struct MapView: UIViewRepresentable {
@Binding var centerCoordinate: CLLocationCoordinate2D
func makeUIView(context: Context) -> MKMapView {
let mapView = MKMapView()
mapView.delegate = context.coordinator
return mapView
}
func updateUIView(_ uiView: MKMapView, context: Context) {
}
func makeCoordinator() -> Coordinator {
Coordinator(self)
}
class Coordinator: NSObject, MKMapViewDelegate {
var parent: MapView
init(_ parent: MapView) {
self.parent = parent
}
func mapViewDidChangeVisibleRegion(_ mapView: MKMapView) {
parent.centerCoordinate = mapView.centerCoordinate
}
}
}
struct MapView_Previews: PreviewProvider {
static var previews: some View {
MapView(centerCoordinate: .constant(MKPointAnnotation.example.coordinate))
}
}
extension MKPointAnnotation {
static var example: MKPointAnnotation {
let annotation = MKPointAnnotation()
annotation.title = "Current Stay"
annotation.subtitle = "Temporary Maryland Abode"
annotation.coordinate = CLLocationCoordinate2D(latitude: 20, longitude: -70)
return annotation
}
}
输出
# import SentimentIntensityAnalyzer class
import nltk
from nltk.tokenize import word_tokenize, RegexpTokenizer
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
# function to print sentiments
# of the sentence.
def sentiment_scores(sentence):
# Create a SentimentIntensityAnalyzer object.
sid_obj = SentimentIntensityAnalyzer()
# polarity_scores method of SentimentIntensityAnalyzer
# oject gives a sentiment dictionary.
# which contains pos, neg, neu, and compound scores.
sentiment_dict = sid_obj.polarity_scores(sentence)
print("Overall sentiment dictionary is : ", sentiment_dict)
print("sentence was rated as ", sentiment_dict['neg']*100, "% Negative")
print("sentence was rated as ", sentiment_dict['neu']*100, "% Neutral")
print("sentence was rated as ", sentiment_dict['pos']*100, "% Positive")
print("Sentence Overall Rated As", end = " ")
# decide sentiment as positive, negative and neutral
if sentiment_dict['compound'] >= 0.05 :
print("Positive")
elif sentiment_dict['compound'] <= - 0.05 :
print("Negative")
else :
print("Neutral")
# Driver code
if __name__ == "__main__" :
print("\n1st statement :")
sentence = "This is the best movie I have watched ever!"
# function calling
sentiment_scores(sentence)
print("\n2nd Statement :")
sentence = "I went to the market"
sentiment_scores(sentence)
print("\n3rd Statement :")
sentence = "I would not recommend this product to you"
sentiment_scores(sentence)
参考文献：