Sentiment Analysis

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Table of Contents Introduction Examples Credits Introduction Identify and extract sentiment in given 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 . This algorithm takes an input string and assigns a sentiment rating in the range [-1 t0 1] (very negative to very positive). Input: (Required):  JSON Object with the key "document", which contains a sentence or paragraph. (for batch, send as a list of objects). Note: sending a bare string instead of a JSON Object is deprecated from a previous version, and may yield unexpected results Output: Sentiment value between -1 and 1 (very negative to very positive) Examples Example 1. Parameter 1: Positive sentence. {
 "document": "I really like eating ice cream in the morning!"
} Output: [
 "sentiment": 0.474,
 "document": "I really like eating ice cream in the morning!"
] Example 2. Parameter 1: Negative sentence. {
 "document": "I really hate you, you are the worst!"
} Output: [
 "sentiment": -0.855,
 "document": "I really hate you, you are the worst!"
] Example 3. Parameter 1: Batch of sentences. [
 "document": "I really hate you, you are the worst!"
 "document": "I really like eating ice cream in the morning!"
] Output: [
 "sentiment": -0.855,
 "document": "I really hate you, you are the worst!"
 "sentiment": 0.474,
 "document": "I really like eating ice cream in the morning!"
] Credits For more information, please refer to  or Manning, Christopher D., Surdeanu, Mihai, Bauer, John, Finkel, Jenny, Bethard, Steven J., and McClosky, David. 2014.  The Stanford CoreNLP Natural Language Processing Toolkit . In  Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations , pp. 55-60.  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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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/SentimentAnalysis/1.0.3 -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/SentimentAnalysis/1.0.3")
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/SentimentAnalysis/1.0.3");
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/SentimentAnalysis/1.0.3")
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/SentimentAnalysis/1.0.3");
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/SentimentAnalysis/1.0.3')
print algo.pipe(input)
View Python Docs

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("nlp/SentimentAnalysis/1.0.3")
result <- algo$pipe(input)$result
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('nlp/SentimentAnalysis/1.0.3')
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/SentimentAnalysis/1.0.3");
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/SentimentAnalysis/1.0.3") { resp, error in
View Swift Docs
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