Articles-Interestingness based on

Twitter-Sentiments

Group - 7

Kriti Kansal - 201101012

Arpit Bhayani - 201305515

Anirudh Beria - 201001104

Rishabh Gupta - 201307676

Why Interesting Article ?

Explosive growth of online articles.

Higher e-commerce value will result fromo Number of hits (Viewership)

o Relevance

o Interestingness

Twitter : The Microblogging Giant

Popular Social Networking Platform.

A precise and concise source of current affairs.

A preferred platform for expressing sentiments.

Approach

Flavour of Articleo Named-Entity Recognition

o Prominent Entities

Live Twitter Streamo Sentiments

o Trends

Work Flow

Named Entity Recognition

Identify Pure Nounso A word which is never been used as any POS

o A word does not exist in English Language

Identify Pure Noun Phraseo Proximity of word with Pure Nouns

Classifying Named Entitieso Use of Hypernym graphs, WordNet

o Assign classes like Person, organization etc. to entities

Sentiment Analysis

May refer to affective state, intended emotional

communication or judgement of a speaker

Determines the attitude of a speaker with respect to

some topic

Helps in identifying Overall contextual polarity of a

document

Live Twitter Stream used for collecting tweets

corresponding to various named entities for the

sentiment calculation

Sentiment Scores

Open twitter stream to get live tweets for each entity derived for an article

Preprocess Tweets:

o Tweet words cleaning

o Elimination of Stop Words

o Spell correction

Classify into Positive and Negative Tweets

○ If num(Positive_Words>Negative_Words)

then class(Tweet)=Pos_Tweet

○ else

class(Tweet)=Neg_Tweet

For each named entity calculate its sentiment score as:

o Score(Entity)=(num(Pos_Tweet)-num(Neg_Tweet))/Num(Total_Entity_Tweets)

Sentiment Scores …

Interestingness Scores

We come up with an interestingness scores used for ranking of the articles

based on each entity’s sentiment scores belonging to that article as:

I1 = ( ∑ | Score(Entity)) / Total_Entites

o Incorporates Sentiment Of Entity

I2 = I1 + factor * min(num(Pos_Entities), num(Neg_entities) ) /

Total_Article_Tweets

o Higher weight for contrasting Entities as they increase interestingness

I3 = I2* Total_Article_TweetsGreater number of live tweets make article more trending

Final Ranking of the articles is based on the interestingness score I3 as

calculated above

Testing Results

BBC news website dataset was used.

It has 2225 Documents with 9636 entities.

For Named Entity Recognition 88% precision and 81% recall obtained using CoreNLP (Stanford NLP library) as standard.

For Sentiment Analysis 65% of the tweets were classified with right sentiments when manually evaluated.

We do final interestingness scores evaluation based on F-Measure.

F-Measure scores based on manual interestingness classification for a testing data of 100 documents achieved was 0.38.

Future Work

• Batch Processing of Tweets along with Background Live Feed as opposed to only Live Twitter Feeds being used for sentiment analysis currently

• Interestingness is after all subjective, thus interestingness measures taking into account the users preference above the objective interestingness scores is aimed towards

References

• Opinion mining, Sentiment Analysis, and Opinion Spam Detection – Vasudeva Varma

• iScore: Measuring the Interestingness of Articles in a Limited User Environment

• Interestingness Measures for Data Mining: A Survey

• A Survey of Interestingness Measures for Knowledge Discovery

• https://semantria.com/features/entityextraction

• http://en.wikipedia.org/wiki/Namedentity_recognition

• http://en.wikipedia.org/wiki/Sentiment_analysis

Thank You