Classify the sentiment conveyed in text as positive or negative.
Code sample
Java
Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .
To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .
import
com.google.cloud.aiplatform.v1beta1.EndpointName
;
import
com.google.cloud.aiplatform.v1beta1.PredictResponse
;
import
com.google.cloud.aiplatform.v1beta1.PredictionServiceClient
;
import
com.google.cloud.aiplatform.v1beta1.PredictionServiceSettings
;
import
com.google.protobuf. Value
;
import
com.google.protobuf.util. JsonFormat
;
import
java.io.IOException
;
import
java.util.ArrayList
;
import
java.util.List
;
// Text sentiment analysis with a Large Language Model
public
class
PredictTextSentimentSample
{
public
static
void
main
(
String
[]
args
)
throws
IOException
{
// TODO(developer): Replace these variables before running the sample.
// The details of designing text prompts for supported large language models:
// https://cloud.google.com/vertex-ai/docs/generative-ai/text/text-overview
String
instance
=
"{ \"content\": \"I had to compare two versions of Hamlet for my Shakespeare \n"
+
"class and unfortunately I picked this version. Everything from the acting \n"
+
"(the actors deliver most of their lines directly to the camera) to the camera \n"
+
"shots (all medium or close up shots...no scenery shots and very little back \n"
+
"ground in the shots) were absolutely terrible. I watched this over my spring \n"
+
"break and it is very safe to say that I feel that I was gypped out of 114 \n"
+
"minutes of my vacation. Not recommended by any stretch of the imagination.\n"
+
"Classify the sentiment of the message: negative\n"
+
"\n"
+
"Something surprised me about this movie - it was actually original. It was \n"
+
"not the same old recycled crap that comes out of Hollywood every month. I saw \n"
+
"this movie on video because I did not even know about it before I saw it at my \n"
+
"local video store. If you see this movie available - rent it - you will not \n"
+
"regret it.\n"
+
"Classify the sentiment of the message: positive\n"
+
"\n"
+
"My family has watched Arthur Bach stumble and stammer since the movie first \n"
+
"came out. We have most lines memorized. I watched it two weeks ago and still \n"
+
"get tickled at the simple humor and view-at-life that Dudley Moore portrays. \n"
+
"Liza Minelli did a wonderful job as the side kick - though I'm not her \n"
+
"biggest fan. This movie makes me just enjoy watching movies. My favorite scene \n"
+
"is when Arthur is visiting his fiancée's house. His conversation with the \n"
+
"butler and Susan's father is side-spitting. The line from the butler, \n"
+
"\\\"Would you care to wait in the Library\\\" followed by Arthur's reply, \n"
+
"\\\"Yes I would, the bathroom is out of the question\\\", is my NEWMAIL \n"
+
"notification on my computer.\n"
+
"Classify the sentiment of the message: positive\n"
+
"\n"
+
"This Charles outing is decent but this is a pretty low-key performance. Marlon \n"
+
"Brando stands out. There's a subplot with Mira Sorvino and Donald Sutherland \n"
+
"that forgets to develop and it hurts the film a little. I'm still trying to \n"
+
"figure out why Charlie want to change his name.\n"
+
"Classify the sentiment of the message: negative\n"
+
"\n"
+
"Tweet: The Pixel 7 Pro, is too big to fit in my jeans pocket, so I bought new \n"
+
"jeans.\n"
+
"Classify the sentiment of the message: \"}"
;
String
parameters
=
"{\n"
+
" \"temperature\": 0,\n"
+
" \"maxDecodeSteps\": 5,\n"
+
" \"topP\": 0,\n"
+
" \"topK\": 1\n"
+
"}"
;
String
project
=
"YOUR_PROJECT_ID"
;
String
location
=
"us-central1"
;
String
publisher
=
"google"
;
String
model
=
"text-bison@001"
;
predictTextSentiment
(
instance
,
parameters
,
project
,
location
,
publisher
,
model
);
}
static
void
predictTextSentiment
(
String
instance
,
String
parameters
,
String
project
,
String
location
,
String
publisher
,
String
model
)
throws
IOException
{
String
endpoint
=
String
.
format
(
"%s-aiplatform.googleapis.com:443"
,
location
);
PredictionServiceSettings
predictionServiceSettings
=
PredictionServiceSettings
.
newBuilder
().
setEndpoint
(
endpoint
).
build
();
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests.
try
(
PredictionServiceClient
predictionServiceClient
=
PredictionServiceClient
.
create
(
predictionServiceSettings
))
{
final
EndpointName
endpointName
=
EndpointName
.
ofProjectLocationPublisherModelName
(
project
,
location
,
publisher
,
model
);
// Use Value.Builder to convert instance to a dynamically typed value that can be
// processed by the service.
V Value
Builder
instanceValue
=
V Value
newBuilder
();
J JsonFormat
parser
().
merge
(
instance
,
instanceValue
);
L Lis<tValu>e
instances
=
new
ArrayList
<> ();
instances
.
add
(
instanceValue
.
build
());
// Use Value.Builder to convert parameter to a dynamically typed value that can be
// processed by the service.
V Value
Builder
parameterValueBuilder
=
V Value
newBuilder
();
J JsonFormat
parser
().
merge
(
parameters
,
parameterValueBuilder
);
V Value
parameterValue
=
parameterValueBuilder
.
build
();
PredictResponse
predictResponse
=
predictionServiceClient
.
predict
(
endpointName
,
instances
,
parameterValue
);
System
.
out
.
println
(
"Predict Response"
);
System
.
out
.
println
(
predictResponse
);
}
}
}
What's next
To search and filter code samples for other Google Cloud products, see the Google Cloud sample browser .
