Starget: Sentiment Analyzer for EnglishTechnical description

SemanticAnalyzer Group, 2013-11-19www.semanticanalyzer.info

This document describes technical details of sentiment analyzer API for the English language. The component can be served as API or as a library for you applications. You can gain the access to the API by registering with mashape.com. The free demo is available on the SemanticAnalyzer Group web site: http://semanticanalyzer.info/blog/starget-english-sentiment-analysis/

The API has two types of analysis:

Finding accurate sentiment hits, but missing some difficult cases optimizing for precision Finding more, but less accurate, sentiment hits, optimizing for recall

The precision is 75-85% at 20% recall. Using high precision classifier may especially suit your project where human analysts are annotating sentiment of a mass media stream of messages, and you would like to have an automatic tool as a pre-filter. What the tool has missed could be annotated by your human analysts to speed up the data release to your end user and / or report to your boss. On the other hand, if you would like to get a sense of emotions in a big corpora of texts quickly and are comfortable missing some difficult cases with un-obvious sentiment polarity, high recall classifier is your bet.

The sentiment analyzer is implementing a highly customizable and highly adaptable to your domain rule-based approach and is analyzing a sentiment flow orientated towards a user specified target (brand name, person name, abstract entity or any other object). The full version of the algorithm (not online, a separate package) is able to resolve anaphora links (when he or she are referring to an object mentioned earlier in a text) with high accuracy.

Currently the API is attributing a text to one of the three classes NEGATIVE, NEUTRAL, POSITIVE.

Speed of processingServer: Intel(R) Core(TM) i7-3820QM CPU @ 2.70GHz, 16GbOperating system: ubuntu 12.10, Java 1.7.0_21 64 bit server1820 characters/ms (recall optimized)38 characters/ms (precision optimized)

Tests were conducted in a single thread on 498 tweet messages with 13915 words and 69121 characters. Totaltime of execution: 1823 ms for the precision optimized classifier and 38 ms for the recall optimized.

Precision / recall

Rule­based classifier with parsing

positive : P = 0.96; R = 0.13186813186813187; F = 0.2318840579710145

neutral : P = 0.3048245614035088; R = 1.0; F = 0.4672268907563025

negative : P = 0.9411764705882353; R = 0.0903954802259887; F = 0.1649484536082474

Total time taken: 19673 ms; 39 ms per item

Classifier=p

positive : P = 0.7611940298507462; R = 0.2802197802197802; F = 0.4096385542168674

neutral : P = 0.3350253807106599; R = 0.9496402877697842; F = 0.4953095684803002

negative : P = 0.8648648648648649; R = 0.1807909604519774; F = 0.29906542056074764

Total time taken: 1823 ms; 3 ms per item

Classifier=r

positive : P = 0.7423312883435583; R = 0.6648351648351648; F = 0.7014492753623188

neutral : P = 0.6331360946745562; R = 0.7697841726618705; F = 0.6948051948051949

negative : P = 0.7590361445783133; R = 0.711864406779661; F = 0.7346938775510204

Total time taken: 38 ms; 0 ms per item

Input parameters of the APIParameter Description Exampleclassifier Sentiment analysis type: p – to

maximize precision (default); r –to maximize recallOptional

classifier=r

target Specify a word or phrase (bothmust be present in the text) thatis commented about. Sentimenttarget is usually a noun or apronoun.Required

car

context The text that potentially has asentiment hit for the giventargetRequired

I love the car.

Calling the Starget API from curlcurl ­­include ­­request GET'https://tezer­starget­sentiment­analysis.p.mashape.com/?classifier=classifier%3Dr&target=target%3Dcar&context=context%3DI%20love%20the%20car.' \ ­­header "X­Mashape­Authorization: <mashape­key>"

output in JSON format

Examples of detecting sentiment

The default precision classifier is assumed in the below examples.

Input:Context: I love the car.Target: car

Output:

Sentiment: positive

Input:

Context: Sentiment analysis has never been perfect.Target: Sentiment analysis

Output:

Sentiment: negative

Input:

Context: Sentiment analysis has never been so perfect.Target: Sentiment analysis

Output:

Sentiment: positive