Sentiment Analysis

No algorithm description given

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 [0-4] (very negative, negative, neutral, positive, and very positive). Input: (Required): String sentence. Output: Sentiment value between 0-4 (very negative, negative, neutral, positive, and very positive) Examples Example 1. Parameter 1: Positive sentence. "I really like eating ice cream in the morning!" Output: 3 Example 2. Parameter 1: Negative sentece. "I really hate you, you are the worst!" Output: 0 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. 

Tags
(no tags)

Cost Breakdown

0 cr
royalty per call
1 cr
usage per second
avg duration

Cost Calculator

API call duration (sec)
×
API calls
=
Estimated cost
per calls
for large volume discounts
For additional details on how pricing works, see Algorithmia pricing.

No permissions required

This algorithm does not require any special permissions.


To understand more about how algorithm permissions work, see the permissions documentation.

1. Type your input

2. See the result

Running algorithm...

3. Use this 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/0.1.2
View cURL Docs
algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://nlp/SentimentAnalysis/0.1.2 -d '{{input | formatInput:"cli"}}'
View CLI 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/0.1.2");
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/0.1.2")
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/0.1.2")
           .pipe(input)
           .then(function(output) {
             console.log(output);
           });
View Javascript Docs
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://nlp/SentimentAnalysis/0.1.2")
           .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/0.1.2')
print algo.pipe(input)
View Python Docs
library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("nlp/SentimentAnalysis/0.1.2")
result <- algo$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/0.1.2')
puts algo.pipe(input).result
View Ruby Docs
use algorithmia::*;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo('nlp/SentimentAnalysis/0.1.2');
let response = algo.pipe(input);
View Rust Docs
Discussion
  • {{comment.username}}