I downloaded stanford core nlp packages and tried to test it on my machine.
Using command: java -cp "*" -mx1g edu.stanford.nlp.sentiment.SentimentPipeline -file input.txt
I got sentiment result in form of positive
or negative
. input.txt
contains the sentence to be tested.
On more command: java -cp stanford-corenlp-3.3.0.jar;stanford-corenlp-3.3.0-models.jar;xom.jar;joda-time.jar -Xmx600m edu.stanford.nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit,pos,lemma,parse -file input.txt
when executed gives follwing lines :
H:\Drive E\Stanford\stanfor-corenlp-full-2013~>java -cp stanford-corenlp-3.3.0.j
ar;stanford-corenlp-3.3.0-models.jar;xom.jar;joda-time.jar -Xmx600m edu.stanford
.nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit,pos,lemma,parse -file
input.txt
Adding annotator tokenize
Adding annotator ssplit
Adding annotator pos
Reading POS tagger model from edu/stanford/nlp/models/pos-tagger/english-left3wo
rds/english-left3words-distsim.tagger ... done [36.6 sec].
Adding annotator lemma
Adding annotator parse
Loading parser from serialized file edu/stanford/nlp/models/lexparser/englishPCF
G.ser.gz ... done [13.7 sec].
Ready to process: 1 files, skipped 0, total 1
Processing file H:\Drive E\Stanford\stanfor-corenlp-full-2013~\input.txt ... wri
ting to H:\Drive E\Stanford\stanfor-corenlp-full-2013~\input.txt.xml {
Annotating file H:\Drive E\Stanford\stanfor-corenlp-full-2013~\input.txt [13.6
81 seconds]
} [20.280 seconds]
Processed 1 documents
Skipped 0 documents, error annotating 0 documents
Annotation pipeline timing information:
PTBTokenizerAnnotator: 0.4 sec.
WordsToSentencesAnnotator: 0.0 sec.
POSTaggerAnnotator: 1.8 sec.
MorphaAnnotator: 2.2 sec.
ParserAnnotator: 9.1 sec.
TOTAL: 13.6 sec. for 10 tokens at 0.7 tokens/sec.
Pipeline setup: 58.2 sec.
Total time for StanfordCoreNLP pipeline: 79.6 sec.
H:\Drive E\Stanford\stanfor-corenlp-full-2013~>
Could understand. No informative result.
I got one example at : stanford core nlp java output
import java.io.*;
import java.util.*;
import edu.stanford.nlp.io.*;
import edu.stanford.nlp.ling.*;
import edu.stanford.nlp.pipeline.*;
import edu.stanford.nlp.trees.*;
import edu.stanford.nlp.util.*;
public class StanfordCoreNlpDemo {
public static void main(String[] args) throws IOException {
PrintWriter out;
if (args.length > 1) {
out = new PrintWriter(args[1]);
} else {
out = new PrintWriter(System.out);
}
PrintWriter xmlOut = null;
if (args.length > 2) {
xmlOut = new PrintWriter(args[2]);
}
StanfordCoreNLP pipeline = new StanfordCoreNLP();
Annotation annotation;
if (args.length > 0) {
annotation = new Annotation(IOUtils.slurpFileNoExceptions(args[0]));
} else {
annotation = new Annotation("Kosgi Santosh sent an email to Stanford University. He didn't get a reply.");
}
pipeline.annotate(annotation);
pipeline.prettyPrint(annotation, out);
if (xmlOut != null) {
pipeline.xmlPrint(annotation, xmlOut);
}
// An Annotation is a Map and you can get and use the various analyses individually.
// For instance, this gets the parse tree of the first sentence in the text.
List<CoreMap> sentences = annotation.get(CoreAnnotations.SentencesAnnotation.class);
if (sentences != null && sentences.size() > 0) {
CoreMap sentence = sentences.get(0);
Tree tree = sentence.get(TreeCoreAnnotations.TreeAnnotation.class);
out.println();
out.println("The first sentence parsed is:");
tree.pennPrint(out);
}
}
}
Tried to execute it in netbeans with including necessary library. But it always stuck in between or gives exception Exception in thread “main” java.lang.OutOfMemoryError: Java heap space
Thou I set the memory to be allocated in property/run/VM box
Any idea how can I run above java example using command line?
I want to get sentiment score of the example
UPDATE
output of : java -cp "*" -mx1g edu.stanford.nlp.sentiment.SentimentPipeline -file input.txt
out put of: java -cp stanford-corenlp-3.3.0.j
ar;stanford-corenlp-3.3.0-models.jar;xom.jar;joda-time.jar -Xmx600m edu.stanford
.nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit,pos,lemma,parse -file
input.txt
CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations.
The Stanford NLP Group We are a passionate, inclusive group of students and faculty, postdocs and research engineers, who work together on algorithms that allow computers to process, generate, and understand human languages.
Using StanfordNLP to Perform Basic NLP Tasks. StanfordNLP comes with built-in processors to perform five basic NLP tasks: Tokenization. Multi-Word Token Expansion.
This is working fine for me -
Maven Dependencies :
<dependency>
<groupId>edu.stanford.nlp</groupId>
<artifactId>stanford-corenlp</artifactId>
<version>3.5.2</version>
<classifier>models</classifier>
</dependency>
<dependency>
<groupId>edu.stanford.nlp</groupId>
<artifactId>stanford-corenlp</artifactId>
<version>3.5.2</version>
</dependency>
<dependency>
<groupId>edu.stanford.nlp</groupId>
<artifactId>stanford-parser</artifactId>
<version>3.5.2</version>
</dependency>
Java Code :
public static void main(String[] args) throws IOException {
String text = "This World is an amazing place";
Properties props = new Properties();
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, parse, sentiment");
StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
Annotation annotation = pipeline.process(text);
List<CoreMap> sentences = annotation.get(CoreAnnotations.SentencesAnnotation.class);
for (CoreMap sentence : sentences) {
String sentiment = sentence.get(SentimentCoreAnnotations.SentimentClass.class);
System.out.println(sentiment + "\t" + sentence);
}
}
Results :
Very positive This World is an amazing place
You can do the following in your code:
String text = "I am feeling very sad and frustrated.";
Properties props = new Properties();
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, parse, sentiment");
StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
<...>
Annotation annotation = pipeline.process(text);
List<CoreMap> sentences = annotation.get(CoreAnnotations.SentencesAnnotation.class);
for (CoreMap sentence : sentences) {
String sentiment = sentence.get(SentimentCoreAnnotations.SentimentClass.class);
System.out.println(sentiment + "\t" + sentence);
}
It will print the sentiment of the sentence and the sentence itself, e.g. "I am feeling very sad and frustrated.":
Negative I am feeling very sad and frustrated.
You need to add the "sentiment" annotator to the list of annotators:
-annotators tokenize,ssplit,pos,lemma,parse,sentiment
This will add a "sentiment" property to each sentence node in your XML.
Per the example here you need to run the Sentiment Analysis.
java -cp "*" -mx5g edu.stanford.nlp.sentiment.SentimentPipeline -file input.txt
Apparently this is a memory expensive operation, it may not complete with only 1 gigabyte. Then you can use the "Evaluation Tool"
java -cp "*" edu.stanford.nlp.sentiment.Evaluate edu/stanford/nlp/models/sentiment/sentiment.ser.gz input.txt
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