Social Sentiment Analysis

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0. TL;DR This algorithm takes an English sentence and assigns sentiment ratings of "positive", "negative" and "neutral". 1. Introduction Identify and extract sentiment in given English string. Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. Input: (Required):     String sentence *  or  A list of strings ** Output: Sentiment* of given sentence(s). *Note: 4 sentiment types are returned: Positive, negative, neutral & compound. The first three sentiments scale from 0 to 1. Compound sentiment is the overall sentiment, where it scales between -1 to 1, negative to positive respectively. 2. Sentences *String sentence:  A sentence.   (key = "sentence") Example of String Sentence: {
 "sentence": "I really like this website called algorithmia"
} or "I really like this website called algorithmia" **A list of strings:  A list which contains at least 1 string sentence.   (key = "sentenceList") Example List of Sentences: {
 "sentenceList": [
 "I like to fly",
 "I hate waking up early",
 "I enjoy eating Italian food"
} or [ "I like to fly", "I hate waking up early", "I enjoy eating Italian food" ] 2. Output Sentiment of given sentence(s): Returns confidence values for positive and negative sentiment. ( key = "positive" and key = "negative" ) Example of Output: [
 "positive": 0,
 "negative": 0.333,
 "sentence": "I had an horrible experience at this burger place",
 "neutral": 0.667,
 "compound": -0.5423
] 3. Examples Example 1: Parameter 1:   A string sentence. {
 "sentence": "I really like double fudge ice cream"
} Output: [
 "positive": 0.358,
 "negative": 0,
 "sentence": "I really like double fudge ice cream",
 "neutral": 0.642,
 "compound": 0.4201
] Example 2: Parameter 1:   A list of strings. {
 "sentenceList": [
 "I like double cheese pizza",
 "I love black coffee and donuts",
 "I don't want to have diabetes"
} Output: [
 "positive": 0.455,
 "negative": 0,
 "sentence": "I like double cheese pizza",
 "neutral": 0.545,
 "compound": 0.3612
 "positive": 0.512,
 "negative": 0,
 "sentence": "I love black coffee and donuts",
 "neutral": 0.488,
 "compound": 0.6369
 "positive": 0,
 "negative": 0.234,
 "sentence": "I don't want to have diabetes",
 "neutral": 0.766,
 "compound": -0.0572
] 4. Credits For more information, please refer to  or  Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text . Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.  

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1. Type your input

2. See the result

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3. Use this algorithm

curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY'
View cURL Docs
algo auth
algo run algo://nlp/SocialSentimentAnalysis/0.1.4 -d '{{input | formatInput:"cli"}}'
View CLI Docs
import (
  algorithmia ""

input := {{input | formatInput:"go"}}

var client = algorithmia.NewClient("YOUR_API_KEY", "")
algo, _ := client.Algo("algo://nlp/SocialSentimentAnalysis/0.1.4")
resp, _ := algo.Pipe(input)
response := resp.(*algorithmia.AlgoResponse)
View Go Docs
import com.algorithmia.*;
import com.algorithmia.algo.*;

String input = "{{input | formatInput:"java"}}";
AlgorithmiaClient client = Algorithmia.client("YOUR_API_KEY");
Algorithm algo = client.algo("algo://nlp/SocialSentimentAnalysis/0.1.4");
AlgoResponse result = algo.pipeJson(input);
View Java Docs
import com.algorithmia._
import com.algorithmia.algo._

val input = {{input | formatInput:"scala"}}
val client = Algorithmia.client("YOUR_API_KEY")
val algo = client.algo("algo://nlp/SocialSentimentAnalysis/0.1.4")
val result = algo.pipeJson(input)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(output) {
View Javascript Docs
using Algorithmia;

var input = "{{input | formatInput:"cs"}}";
var client = new Client("YOUR_API_KEY");
var algorithm = client.algo("algo://nlp/SocialSentimentAnalysis/0.1.4");
var response = algorithm.pipe<object>(input);
View .NET/C# Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(response) {
View NodeJS Docs
import Algorithmia

input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('nlp/SocialSentimentAnalysis/0.1.4')
print algo.pipe(input)
View Python Docs

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("nlp/SocialSentimentAnalysis/0.1.4")
result <- algo$pipe(input)$result
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('nlp/SocialSentimentAnalysis/0.1.4')
puts algo.pipe(input).result
View Ruby Docs
use algorithmia::Algorithmia;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo("nlp/SocialSentimentAnalysis/0.1.4");
let response = algo.pipe(input);
View Rust Docs
import Algorithmia

let input = "{{input | formatInput:"swift"}}";
let client = Algorithmia.client(simpleKey: "YOUR_API_KEY")
let algo = client.algo(algoUri: "nlp/SocialSentimentAnalysis/0.1.4") { resp, error in
View Swift Docs
  • {{comment.username}}