Steps:
To work with Spark, we must first create a SparkSession object.
import org.apache.spark.sql.SparkSession
val spark = SparkSession.builder()
.appName(”SentimentAnalysis”)
.master(”local[*]”).getOrCreate()
The next step is to load the dataset of text data we will analyze for
sentiment. Any type of text data can be used, such as customer
reviews, social media posts, or other texts. A dataset of customer
reviews could be loaded as follows:
val custReviews = spark.read.option(”header”,”true”)
.csv(”customer_reviews_data.csv”)
As a next step, we need to preprocess the text data in order to
prepare it for sentiment analysis. The process usually involves
removing any irrelevant information, such as punctuation and stop
words, and converting the text data into a machine-readable format.
The text data could be preprocessed as follows:
import org.apache.spark.ml.feature.StopWordsRemover
import org.apache.spark.ml.feature.Tokenizer
val tokenizer = new Tokenizer().setInputCol(”text”)
.setOutputCol(”words”)
val remover = new StopWordsRemover().setInputCol(”words”)
.setOutputCol(”filtered”)
val words = tokenizer.transform(custReviews)
val filteredWords = remover.transform(words)
The preprocessed text data can then be used to train a machine
learning model for sentiment analysis. As an example, we could train a
Logistic Regression model as follows:
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.feature.{HashingTF,
IDF}
val hashingTF = new
HashingTF().setInputCol(”filtered”)
.setOutputCol(”rawFeatures”)
val idf = new IDF().setInputCol(”rawFeatures”)
.setOutputCol(”features”)
val lr = new LogisticRegression().setLabelCol(”label”)
.setFeaturesCol(”features”)
val pipeline = new Pipeline().setStages(Array(tokenizer, remover, hashingTF,idf, lr))
val model = pipeline.fit(trainingData)
Finally, we can use the trained model to predict sentiment from new
text data. As an example, we could predict the sentiment of a new
customer review as follows:
val newReview = Seq(”This product is great!”).toDF(”text”)
val prediction = model.transform(newReview)
In the prediction DataFrame, we will present a predicted label
for the new review, as well as a probability score indicating the degree
of confidence the model has in its prediction. In analyzing the
predicted labels for a large dataset of text data, we can gain insights
into the sentiment of the text data and use these insights to improve
our products.