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Concept Cloud-based Sentiment Visualization for Financial Reviews

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DECON 2019
https://www.decision-economics.net/

Published in: Data & Analytics
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Concept Cloud-based Sentiment Visualization for Financial Reviews

  1. 1. Concept Cloud-based Sentiment Visualization for Financial Reviews Tomoki Ito*, Kota Tsubouchi**, Hiroki Sakaji*, Tatsuo Yamashita**, Kiyoshi Izumi* ** Graduate School of Engineering, The University of Tokyo * Yahoo Japan Corporation
  2. 2. Back ground • Online reviews are useful for decision making in the investment. • e.g., micro-blogs, SNS, and news articles…
  3. 3. Difficulty in Reading Large Volume of Reviews • To read all the posts should not be practical • the volume of the posts is sometimes very large.
  4. 4. Difficulty in Reading Large Volume of Reviews • To read all the posts should not be practical • the volume of the posts is sometimes very large. • Framework for visualizing the summary of the financial reviews is necessary
  5. 5. What is important for decision making in the investment ? • In the decision making process, the following two types of sentiments are important • Word-level sentiment score • Concept-level sentiment score
  6. 6. What is important for decision making in the investment ? • In the decision making process, the following two types of sentiments are important • Word-level sentiment score • Concept-level sentiment score
  7. 7. Word-level sentiment • Word-level sentiment means the sentiment scores in word units In total, we are in a bull market. 0.1 0.1 0.2 0.0 0.3 1.2 -0.1
  8. 8. Word-level sentiment • Word-level sentiment means the sentiment scores in word units • We should consider the context in this score assignment • e.g., sentiment shift by “not” In total, we are in a bull market. 0.1 0.1 0.2 0.0 0.3 1.2 -0.1 In total, we are not in a bull market. 0.1 0.1 0.2 0.0 0.3 -0.1 -0.1
  9. 9. What is important for decision making in the investment ? • In the decision making process, the following two types of sentiments are important • Word-level sentiment score • Concept-level sentiment score
  10. 10. Concept-level Sentiment • Concept-level sentiment means the sentiment scores in concept units • Concept means a set of similar words Up, Down, ↗︎↗︎ Delicious, Nasty, Palatable, … Clean, Dirty Trend: 0.5 Taste: -0.1 Cleanness: -0.2 Reviews
  11. 11. What is important for decision making in the investment ? • In the decision making process, the following two types of sentiments are important • Word-level sentiment score • Concept-level sentiment score
  12. 12. Purpose • This study aims to develop a method for visualizing • Word-level sentiment score, and • Concept-level sentiment score at the same time in a user-friendly way
  13. 13. Our Approach • We propose a novel text-visualization framework called CCSV Concept Cloud-based Sentiment Visualization
  14. 14. CCSV Example • Using CCSV, we can summarize reviews as follows (The price was inversed www) (The price was inversed.) (Go down below 3000yen. I cannot buy now.) ・ ・ ・ (Over 1000 reviews in five days)
  15. 15. CCSV Example • Using CCSV, we can summarize reviews as follows text-visualization results for a set of reviews for trading company X in September 25th, 2017 and September 30th, 2017 extracted from the Yahoo Financial Micro-blogs. • Color • Red: Positive Blue: Negative • Size: Volume of Sentiment
  16. 16. Contribution Our Contribution is summarized as follows • We propose a novel text-visualization framework called CCSV • We experimentally evaluated the validity of the CSCV using real dataset
  17. 17. Contribution Our Contribution is summarized as follows • We propose a novel text-visualization framework called CCSV • We experimentally evaluated the validity of the CCSV
  18. 18. Concept Cloud-based Sentiment Visualization • CCSV is constructed from the following three parts 1. Word-level sentiment Extraction 2. Concept-level sentiment Extraction 3. Word and Concept-level sentiment Visualization
  19. 19. Concept Cloud-based Sentiment Visualization • CCSV is constructed from the following three parts 1. Word-level sentiment Extraction 2. Concept-level sentiment Extraction 3. Word and Concept-level sentiment Visualization
  20. 20. Word-level sentiment Extraction • This step addresses the following contextual word-level sentiment score assignment task Input: In total, we are not in a bull market. In total, we are not in a bull market. ( Sentiment influence: polarity of “bull” is shifted by not) In total, we are not in a bull market. (Red and blue words has positive and negative sentiments, respectively ) Original Word-level Sentiment (sentiment score before considering contexts): Contextual Word-level Sentiment (sentiment score after considering contexts)
  21. 21. Task Setting • This step aims to assign word-level sentiment scores using only a text corpus dataset including reviews and their positive or negative sentiment tags In total, we are in a bull market. Review: Tag: Positive • We decided this task setting considering the practicality Text corpus dataset
  22. 22. Previous approach in Word-level sentiment Extraction • Previous works[Vo 2016, Li 2017] address this task by automatically developing a word sentiment score dictionary • However, they cannot consider contexts Input: In total, we are not in a bull market. In total, we are not in a bull market. Cannot consider contexts
  23. 23. Our approach • We solve this task by estimating • P (•) : Original word-level sentiment • R (•) : Contextual word-level sentiment using the LRP method [L. Arras. et. al., 2017] with the RNN model Document dataset: {Di}N i=1 where Di = {wt i }N i=1 Sentiment tag:
  24. 24. Our approach • We solve this challenge by estimating • P (•) : Original word-level sentiment • R (•) : Contextual word-level sentiment using the LRP method [L. Arras. et. al., 2017] with the RNN model Document dataset: {Di}N i=1 where Di = {wt i }N i=1 Sentiment tag:
  25. 25. LRP-based Approach Process • We estimate R (•) as follows
  26. 26. LRP-based Approach Process • We estimate R (•) as follows 1. Develop a RNN model with LSTM cells using Text corpus dataset including document and their positive or negative sentiment tags
  27. 27. LRP-based Approach Process • We estimate R (•) as follows 1. Develop a RNN model with LSTM cells using Text corpus dataset including document and their positive or negative sentiment tags 2. Estimate • R (•) : Contextual word-level sentiment using the LRP method [L. Arras. et. al., 2017] with the RNN model In total, we are in a bull market. R (•) : LRP + RNN (LSTM) 0.1 0.1 0.2 0.0 0.3 1.2 -0.1
  28. 28. Layer-wise Representation Propagation(LRP) • LRP is the method for interpreting Neural Networks • LRP calculates the relevance score of the input value to the output value • LRP can be used in the RNN with LSTM cells • the relevance score of each term from the LRP with the RNN is expected to consider contexts market is bull Positive Negative OutputInput (L. Arras. et. al., 2017) 28
  29. 29. Concept Cloud-based Sentiment Visualization • CCSV is constructed from the following three parts 1. Word-level sentiment Extraction • using the LRP method 2. Concept-level sentiment Extraction • with the word-level sentiments and K-means clustering method 3. Word and Concept-level sentiment Visualization • using Word Cloud method
  30. 30. Concept-level sentiment Extraction • This step calculates the concept-level sentiment score using the k-means clustering method Up: 0.5 Down: -0.2 ↗︎↗︎ : 0.6 delicious: 0.6 nasty: -0.7 Palatable: +1.5 Clean: +0.3 Dirty: -0.2 Reviews LRP Up: 0.5 Down: -0.2 ↗︎↗︎ : 0.6 delicious: 0.6 nasty: -0.7 Palatable: +1.5 Clean: +0.3 Dirty: -0.2 0.9 +1.4 +0.1 K-means method with word2vec
  31. 31. Concept Cloud-based Sentiment Visualization • CCSV is constructed from the following three parts 1. Word-level sentiment Extraction • using the LRP method 2. Concept-level sentiment Extraction • with the word-level sentiments and K-means clustering method 3. Word and Concept-level sentiment Visualization • using Word Cloud method
  32. 32. Word and Concept-level sentiment Visualization • This step visualize the word-level and concept-level sentiment scores using Tag Cloud Approach Up: 0.5 Down: -0.2 ↗︎↗︎ : 0.6 delicious: 0.6 nasty: -0.7 Palatable: +1.5 Clean: +0.3 Dirty: -0.2 0.9 +1.4 +0.1 Up Down ↗︎ ↗︎ Deliciousnasty Palatable Clean Dirty • Color • Red: Positive Blue: Negative • Size: Volume of Sentiment
  33. 33. Contribution Our Contribution is summarized as follows • We propose a novel text-visualization framework called CCSV • We experimentally evaluated the validity of the CCSV
  34. 34. Experimental Evaluation • We evaluated our method from two aspects using real textual datasets • Original Sentiment assignment property • Contextual sentiment assignment property
  35. 35. Dataset • We evaluated the validity of our approach using the following dataset • Text Corpus • Economic dataset: Current economy watchers survey • Train: 20,000 positive posts and 20,000 negative posts • Valid: 2,000 positive posts and 2,000 negative posts • Test: 4,000 positive posts and 4,000 negative posts • Yahoo dataset: Yahoo Finance micro-blogs between September • Train: 30,612 positive posts and 9,388 negative posts • Valid: 3,387 positive posts and 1,613 negative posts • Test: 7,538 positive posts and 2,462 negative posts
  36. 36. Experimental Evaluation • We evaluated our method from three aspects using real textual datasets • Original Sentiment assignment property • Contextual sentiment assignment property
  37. 37. Original Sentiment assignment property • How accurately P (•) presents the positive or negative polarity of each term in the word polarity list • Economic word polarity list • 348 positive and 391 negative words • We used this list when we estimated P (•) using the Economic dataset • Yahoo word polarity list • 422 positive and 372 negative words • We used this list when we estimated P (•) using the Yahoo dataset Good: Positive Bad: Negative Great: Positive Bullish: Positive ・ ・ ・ Word polarity list
  38. 38. Comparison Method • We compared our method with the following comparison methods • Word-level sentiment score assignment methods • PMI • FLW [D. T. Vo et. al., 2016] • SONN [Q. Li et. al., 2017]
  39. 39. Result 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 PMI LFW SONN LRP (Our Approach) Economic Dataset Yahoo Dataset Our method outperformed the other word-level sentiment assignment approaches MacroF1score
  40. 40. Experimental Evaluation • We evaluated our method from three aspects using real textual datasets • Original Sentiment assignment property • Contextual sentiment assignment property
  41. 41. Contextual Sentiment assignment property • How accurately the sum of the contextual word-level sentiment scores in a term of each review in test dataset presents the positive or negative polarity of the review In total, we are in a bull market. R (•) :LRP + RNN (LSTM) 0.1 0.1 0.2 0.0 0.3 1.2 -0.1 0.1 + 0.1 + 0.2 + 0,0 + 0.3 + 1.2 + -0.1 = 1.8 Positive Accurate ?
  42. 42. Comparison Method • We compared our method with the following comparison methods • Word-level sentiment score assignment methods • PMI • FLW [D. T. Vo et. al., 2016] • SONN [Q. Li et. al., 2017] • LR: Logistic Regression • RNN with LSTM cells
  43. 43. Result 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 LR PMI LFW SONN LRP (Our Approach) LSTM Economic Dataset Yahoo Dataset Our method outperformed the other word-level sentiment assignment approaches (did not work better than the LSTM) MacroF1score
  44. 44. CCSV Example • Using CCSV, we can summarize reviews as follows Text-visualization results for a set of reviews for trading company X in September 25th, 2017 and September 30th, 2017 extracted from the Yahoo Financial Micro-blogs. Useful for decision making process in investment • Color Red: Positive Blue: Negative • Size: Volume of Sentiment
  45. 45. Conclusion • Summary • We propose a novel text-visualization framework called CCSV • We experimentally evaluated the validity of the CCSV • Future work • We will modify the CCSV more user-friendly • We will apply this approach to other languages
  46. 46. Previous work • Rule base ? • Dictionary base ?
  47. 47. Previous work • Rule base ? • Dictionary base ? Riles in Financial domain are too specific and specialized
  48. 48. To understand the financial text, we need the specialized dictionary.
  49. 49. Challenge • To consider both original word-level sentiment and contexts is challenging problem !
  50. 50. Motivation • To understand the financial text, we need • the specialized dictionary for word-level sentiment • Understand sentiment shift
  51. 51. Back ground • Non-experts often fail to understand financial text
  52. 52. Layer-wise Representation Propagation(LRP) • Calculate the relevance score of the input value to the output value by • starting from the output layer of the neural network and • backpropagating this quantity up to the input layer. market is bull Positive Negative OutputInput (L. Arras. et. al., 2017) 52
  53. 53. Layer-wise Representation Propagation(LRP) zj Linear Connections zg zs zj Multiplicative Connections ◎ zi wij Sigoid 関数などにより(-1,1) に変 換 53 (L. Arras. et. al., 2017)
  54. 54. Layer-wise Representation Propagation(LRP) zj Linear Connections zg zs zj Multiplicative Connections ◎ zi wij 54 (L. Arras. et. al., 2017)
  55. 55. LRP-based Approach Process • We estimate R (•) as follows 1. Develop a RNN model with LSTM cells using Text corpus dataset including document and their positive or negative sentiment tags In total, we are in a bull market. Review: Tag: Positive Text corpus dataset
  56. 56. Previous approach in Word-level sentiment Extraction • Previous works[Vo 2016, Li 2017] address this task by automatically developing a word sentiment score dictionary • However, they cannot consider contexts Input: In total, we are not in a bull market. In total, we are not in a bull market. Cannot consider contexts

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