Use of Sentiment Analysis via trained set, in message sent to

Facebook messenger bot created via AWS lambda/Stamplay.

IntroductionWe have created a Facebook messenger bot. Bot doesn’t know how to respond according to situations but we have to find the general idea.So we are introducing the Sentiment Analysis on the message and figure out the proper response according to the user message and record the overall response or attitude towards the bot.

Why bots ?

~90% of our time on mobile is spent on email and messaging platforms

3rd generation platform• Bots: New Apps• Voice: User Interface• AI: Protocol (linguistic analysis, image recognition, concept inference,

sentiment analysis, text enrichments and many other techniques)• Messaging Apps: Browsers (Slack, Skype or Facebook Messenger)

Basic Idea/FeaturesThe basic idea is to create a bot that can respond to human sentiments and also provide the feedback to us if the bot is receiving positive or negative response. Bot could send message to user according to their response.We are using “Sentiwordnet” and java to figure out the sentiment. Sentiwordnet is a huge training set, that contain the words , stemmed words and phrases with their corresponding positive and/or negative value.

Dataset/SourceThere are 3 sources for data.1) SentiWordText

We can consider the sentiwordtext as the training set data, We have altered the sentiwordtext in several places for to make more suitable for out purpose. It is stored in Amazon S3, to make it accessible from AWS lambda.

2) User Message to botThe test set here is the message the user sends to the messenger. We compare the phrase and word again set in sentiwordtext to get the sentiment value of the message and process it.

3) AWS DynamoDBThe messages and the calculated sentiment is saved in the dynamodb along with userid, so later it can be scanned easily to get the particular user’s data.

Data Source Attribute• Sentiwordnet: Sentiwordnet had 6 attributes namely, POS, ID,

PosScore, NegScore, SynsetTerms, Gloss. We used java to isolate the word from Sysset Terms and stored the PosScore and NegScore for the word. They are all textual file in tab separated format• User Message: User message consisted of many attribute in JSON

format, We are interested in message and facebook userId.• AWS DynamoDB: dynamodb is NOSql, so there are no fix schema,

how ever we created object with attributes. ID which is primary key and unique(set it as timestamp), facebookID (secondary key, used for searching),message and sentiment value.

ToolsThere are lots of tools involved in this project1) Facebook Messenger bot: The tool which is used to interact with user and get the input data

to calculate the sentiment.2) Stamplay: A online tool used to get the message from bot and perform the AWS lambda

function. After the lambda function return result, carryout corresponding reply.3) AWS lambda: Functionality provided by Amazon web services, by the means of which we

can perform java code in Amazon server without having to get much server time.4) Sentiwordnet: The trained set text file that contains the trained set with the stemmed words

and their corresponding sentiment value. 5) AWS DynamoDb: The NOSql database by Amazon, where we store message, its value and

user id6) AWS S3: The Amazon storeage facility, where the sentiwordnet is stored. Lambda uses this

service to get sentiwordnet.txt

Messenger

Messages

idPK

FBid

msg

Sentiwordnet

Stamplay

S3

Lambda function

I m very happy today

Webhook

I m very happy today

I m very happy today, 0.87)

getSentiment( I m very happy today

getScore( I m happy today)

sentiment

Analysis/AlgorithmFor the purpose of analyzing the words, we are using Bag of Words Approach, where most common words are assigned certain value. The sentiwordnet training set created a hash of the words with vector about the stemmed word and the value of sentiment it represents. We the use the data and compare it against the hash and we find the sentiment value for a given phrase.The value is then measured against the values set for positive, negative, and neutral response. The messages are all saved with their corresponding sentiment value.Certain trigger word currently “recommend” will get the data from the database, and calculate the average for the messages and return proper phrase to send to user. Stamplay sends the message to user

SentiWordnetWordNet is lexical database which groups the synonyms of words namely, synsets. SENTIWORDNET 3.0 glosses from the Princeton WordNet Gloss Corpus, according to which a gloss is actually a sequence of WORDNET synsets, thus based on “bag of synsets” model, unlike the “bag of words” approach in previous version. Basically how it populates its data is, it gets positive and negative synsets for a synset within certain k distance from itself. The glosses are used to classify the positive,negative or neutral rating for the synset.http://sentiwordnet.isti.cnr.it/

ResultWe hope the result would be a smarter bot which will be able to respond according to users current sentiment. Finding sentiment would help in better sales pitch, use of softer language, word of appreciation towards user etc.The way user interacts with the bot could help us know the feedback and improve the bot in future according to the feedback.