Sentiment Analysis - Algorithm by nlp

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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 http://nlp.stanford.edu/software/corenlp.shtml 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 http://www.socialai.gatech.edu/ 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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Running algorithm...

curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY' https://api.algorithmia.com/v1/algo/nlp/SentimentAnalysis/1.0.3

View cURL Docs

algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://nlp/SentimentAnalysis/1.0.3 -d '{{input | formatInput:"cli"}}'

View CLI Docs

import (
  algorithmia "github.com/algorithmiaio/algorithmia-go"
)

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)
fmt.Println(response.Result)

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);
System.out.println(result.asJsonString());

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)
System.out.println(result.asJsonString)

View Scala Docs

var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://nlp/SentimentAnalysis/1.0.3")
           .pipe(input)
           .then(function(output) {
             console.log(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);
Console.WriteLine(response.result);

View .NET/C# Docs

var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://nlp/SentimentAnalysis/1.0.3")
           .pipe(input)
           .then(function(response) {
             console.log(response.get());
           });

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

library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("nlp/SentimentAnalysis/1.0.3")
result <- algo$pipe(input)$result
print(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
  print(resp)
}

View Swift Docs

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