Sentiment Analysis POLITEKNIK ELEKTRONIKA NEGERI SURABAYA DEPARTEMEN TEKNIK INFORMATIKA DAN KOMPUTER PROGRAM STUDI TEKNIK INFORMATIKA

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1 POLITEKNIK ELEKTRONIKA NEGERI SURABAYA DEPARTEMEN TEKNIK INFORMATIKA DAN KOMPUTER PROGRAM STUDI TEKNIK INFORMATIKA Sentiment Analysis Zulhaydar Fairozal Akbar zfakbar.lecturer.pens.ac.id 2017

2 Introduction

3 Introduction

4 Introduction

5 Introduction

6 Sentiment Analysis sentimental analysis is the task to identify an e-text (text in the form of electronic data such as comments, reviews or messages) to be positive, neutral, or negative. the computational treatment of opinion, sentiment, and subjectivity in text the process of determining whether a piece of writing (product/movie review, tweet, etc.) is positive, negative or neutral.

7 Sentiment Analysis It can be used to identify the customer or follower's attitude towards a brand through the use of variables such as context, tone, emotion, etc. Marketers can use sentiment analysis to research public opinion of their company and products, or to analyze customer satisfaction. Organizations can also use this analysis to gather critical feedback about problems in newly released products. Not only helps companies understand how they re doing with their customers, it also gives them a better picture of how they stack up against their competitors.

8 Sentiment Analysis Consumer information Product reviews Marketing Consumer attitudes Trends Politics Politicians want to know voters views Voters want to know policitians stances and who else supports them Social Find like-minded individuals or communities

9 Classifying Sentiment

10 Classifying Sentiment

11 Challenges Tweets are highly unstructured and also non-grammatical Out of Dictionary words Use of Sarcasm which increases ambiguity Extensive use of Slang Language eg. Thn, u, r

12 Challenges Consider : "The watch isn't water resistant" - In a product review this could be negative. "As much use as a trapdoor on a lifeboat" - negative but not obvious to the machine. "The canon camera is better than the Fisher Price one" - comparisons are hard to classify. "imo the ice cream is luuurrrrrrvely" - slang and the way we communicate in general needs to be processed.

13 Approaches Machine Learning Naïve Baiyes Maxium Entropy Classifier SVM Unsupervised methods Use Lexicons

14 Process Tweet Graphical Representation Data Preprocessing Classified Tweets Classification Algorithm

15 Data Collection The data from twitter can be retrieved in many ways like, using Twitter Search API. The data retrieved in JSON format.

16 Data Preprocessing The preprocessing of tweets is a very important part in SA. It is a process to remove the unwanted words from tweets that does not amount to any sentiments. Emotional Icons : 170 emoticons; identified emotional icons and remove them URLs : does not signify any sentiment; replaced it with a word Stop words : words as a, is, the ; does not indicate any sentiment UserNames and HashTags symbol before the username and # for topic Repeated Letters : happpppyyyy, haaapppyy, haaaapppy into the token happy Slag words : non English Words

17 Classification Algorithm Naïve Bayes A popular supervised classification paradigm. A well-known Bayesian network classifier is the Naïve Bayes classifier is a probabilistic classifier based on the Bayes theorem. An advantage of Naïve Bayes is that it only requires a small amount of training data to estimate the parameters necessary for classification.

18 Naïve Bayes Bila terdapat dua kejadian yang terpisah (misalkan A dan B) maka teorema Bayes dirumuskan sebagai berikut : P A B = P B A P(A) P(B) Prior : nilai probabilitas yang seseorang yakini benar sebelum melakukan eksperimen terhadap sesuatu Posterior : Bila kemudian dilakukan eksperimen dan mengakibatkan adanya perubahan atau perbaikan terhadap nilai probabilitas.

19 Example Data Training No. Jenis Kelamin StatLus Mhs Pernikahan IPK LULUS? 1 L MAHASISWA BELUM 3.17 TEPAT 2 L BEKERJA BELUM 3.30 TEPAT 3 P MAHASISWA BELUM 3.01 TEPAT 4 P MAHASISWA MENIKAH 3.25 TEPAT 5 L BEKERJA MENIKAH 3.20 TEPAT 6 L BEKERJA MENIKAH 2.50 TERLAMBAT 7 P BEKERJA MENIKAH 3.00 TERLAMBAT 8 P BEKERJA BELUM 2.70 TERLAMBAT 9 L BEKERJA BELUM 2.40 TERLAMBAT 10 P MAHASISWA MENIKAH 2.50 TERLAMBAT 11 P MAHASISWA BELUM 2.50 TERLAMBAT 12 P MAHASISWA BELUM 3.50 TEPAT 13 L BEKERJA MENIKAH 3.30 TEPAT 14 L MAHASISWA MENIKAH 3.25 TEPAT 15 L MAHASISWA BELUM 2.30 TERLAMBAT

20 Example Data Test : Kelamin : Laki-laki Status : Mahasiswa Pernikahan : Belum IPK : 2.70 Lulus :?

21 Example Menghitung Posterior Tepat dan Terlambat P(Y=TEPAT) = 8/15 P(Y=TERLAMBAT) = 7/15 Menghitung Posterior Jenis Kelamin = L, Y = Tepat dan Terlambat P(Jenis_Kelamin = L Y = Tepat ) = 5/8 P(Jenis_Kelamin = L Y = Terlambat) = 3/7 Menghitung Posterior Status Mahasiswa = Mahasiswa, Y = Tepat dan Terlambat P(Status Mhs = Mahasiswa Y = Tepat ) = 5/8 P(Status Mhs = Mahasiswa Y = Terlambat) = 3/7

22 Example Menghitung Posterior Pernikahan = Belum, Y = Tepat dan Terlambat P(Pernikahan = Belum Y = Tepat ) = 4/8 P(Pernikahan = Belum Y = Terlambat) = 4/7 Menghitung Posterior IPK = 2.70, Y = Tepat dan Terlambat P(IPK = 2.70 Y = Tepat ) = 0/8 P(IPK = 2.70 Y = Terlambat) = 1/7

23 Example Mencari nilai Posterior Tepat dan Terlambat dengan cara mengkalikan semua posterior atribut data. P (Lulus = Tepat ) = P(L Tepat). P(Mahasiswa Tepat). P(Belum Tepat). P(2.70 Tepat) 5/8. 5/8. 4/8. 0/8. 8/15 = 0 P (Lulus = Terlambat) = P(L Terlambat). P(Mahasiswa Terlambat). P(Belum Terlambat). P(2.70 Terlambat) 3/7. 3/7. 4/7. 1/7. 7/15 = Karena nilai P(Lulus Terlambat) > P(Lulus Tepat) maka data test diklasifikasikan ke Terlambat. Jenis Kelamin StatLus Mhs Pernikahan IPK LULUS? L MAHASISWA BELUM 2.70 Terlambat

24 Graphical Representation Sentiment Analysis Positive Negative Neutral

25 Tugas Data Training No. X1 X2 X3 Class 1 Yes SINGLE 125 NO 2 No MARRIED 100 NO 3 No SINGLE 70 NO 4 Yes MARRIED 120 NO 5 No DIVORCE 95 YES 6 No MARRIED 60 NO 7 Yes DIVORCE 220 NO 8 No SINGLE 85 YES 9 No MARRIED 75 NO 10 No SINGLE 90 YES Data Test X1 X2 X3 Class No Married 120?

26 Tugas Data Training No. Color Type Origin Stolen? 1 Red Sports Domestic Yes 2 Red Sports Domestic No 3 Red Sports Domestic Yes 4 Yellow Sports Domestic No 5 Yellow Sports Imported Yes 6 Yellow SUV Imported No 7 Yellow SUV Imported Yes 8 Yellow SUV Domestic No 9 Red SUV Imported No 10 Red Sports Imported Yes Data Test Color Type Origin Stolen? Red SUV Domestic?