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Best results for cases 1–3 with two-class labels_
| Dataset A: Tweets with article's titles | |||
|---|---|---|---|
| Case Number | Model | Accuracy | F-1 Score |
| 1 | Random Forest | 0.81 | 0.81 |
| 2 | Random Forest | 0.83 | 0.83 |
| 3 | Random Forest | 0.85 | 0.85 |
Sentiment distribution of articles using SentiStrength and Sentiment140 libraries_
| Sentiment library | Metric for multiple sentiments | Number of positive sentiments | Number of negative sentiments | Number of neutral sentiments |
|---|---|---|---|---|
| SentiStrength | mean | 11,443 (≈ 7.7%) | 31,212 (≈ 21%) | 106,057 (≈ 71.3%) |
| SentiStrength | median | 14,905 (≈ 10%) | 39,091 (≈ 26.3%) | 94,716 (≈ 63.7%) |
| Sentiment140 | mean | 3,528 (≈ 2.4%) | 6,254 (≈ 4.2%) | 138,930 (≈ 93.4%) |
| Sentiment140 | median | 3,544 (≈ 2.4%) | 3,168 (≈ 2.1%) | 142,000 (≈ 95.5%) |
Best results for cases 1–3 with three labels_
| Dataset A: Tweets with article's titles | |||
|---|---|---|---|
| Case Number | Model | Accuracy | F-1 Score |
| 1 | Random Forest | 0.46 | 0.46 |
| 2 | Random Forest | 0.49 | 0.45 |
| 3 | Random Forest | 0.68 | 0.66 |
Sentiments on dataset B using different libraries and metrics_
| Experiment | Sentiment library | Metric for multiple sentiments | Number of positive sentiments | Number of negative sentiments | Number of neutral sentiments |
|---|---|---|---|---|---|
| case 1 | VADER | mean | 44,866 (≈ 42.4%) | 26,664 (≈ 25.1%) | 34,304 (≈ 32.4%) |
| case 2 | VADER | median | 38,038 (≈ 35.9%) | 23,124 (≈ 21.8%) | 44,672 (≈ 42.2%) |
| case 3 | TextBlob | mean | 54,169 (≈ 51.1%) | 11,841 (≈ 11.1%) | 39,824 (≈ 37.6%) |
| case 4 | TextBlob | median | 45,254 (≈ 42.7%) | 9,551 (≈ 9%) | 51,029 (≈ 48.2%) |
Results of the regression models_
| Dataset A: Tweets with article's titles | ||
|---|---|---|
| Model | Mean Squared Error | R-Squared |
| Multiple Linear Regression | 0.091 | 0.008 |
| Decision Tree | 0.189 | −1.051 |
| Random Forest | 0.104 | −0.130 |
| Support Vector Regression | 0.093 | −0.014 |
Segregation of sentiments score_
| Score range | Sentiment |
|---|---|
| [−1,0) | Negative |
| 0 | Neutral |
| (0,1] | Positive |
Examples of sentiment label assignment_
| Article | 1st Tweet and Sentiment | 2nd Tweet and Sentiment | 3rd Tweet and Sentiment | Mean of tweets’ sentiment | Final sentiment class label |
|---|---|---|---|---|---|
| Article 1 | Researchers in Norway investigate mortality risk of individuals after the death of a spouse (−0.7184) | Can you die of a broken heart? If your spouse dies, your death risk substantially increases (−0.9186) | A sad study: spouses much more likely to die after being widowed (−0.885) | −0.8407 | Negative |
| Article 2 | Presentation of the ABC Best Paper Award 2013 to Sherrie Elzey. Read the winning paper (0.9022) | ABC Best Paper Award 2013 goes to lead authors Sherrie Elzey and De-Hao Tsai. Read their article for free (0.9001) | NA | 0.90115 | Positive |
| Article 3 | Latest article from our research team has been published about using School Function Assessment! (0) | Article on using School Function Assessment now online (0) | NA | 0 | Neutral |
Selected features from the Altmetrics dataset_
| Feature | Description |
|---|---|
| Scopus subject | Subject of a research article. |
| Article title | Title of a research article. |
| Article abstract | Abstract of a research article. |
| Abstract length | Number of words in the abstract of a research paper. |
| Follower count | Number of followers a Twitter user has. |
| Author count | Number of authors credited on the research article. |
| Tweet | Tweet about a research article. |
Derived features from the dataset_
| Original feature | Derived feature | Description |
|---|---|---|
| Article title | Title sentiment | Sentiment score of the title of a research article. |
| Article abstract | Abstract sentiment | Sentiment score of a research article abstract. |
| Follower count | Tweet reach | The mean number of followers of each user who tweeted about the research article (i.e. one article can be tweeted by many users, who may differ from each other in the number of followers they have). |
| Tweet | Tweet sentiment | Sentiment score of a tweet related to a research article. |
Sentiments on dataset A using different libraries and metrics_
| Experiment | Sentiment library | Metric for multiple sentiments | Number of positive sentiments | Number of negative sentiments | Number of neutral sentiments |
|---|---|---|---|---|---|
| case 1 | VADER | mean | 55,833 (≈ 37.5%) | 37,957 (≈ 25.5%) | 54,922 (≈ 36.9%) |
| case 2 | VADER | median | 45,606 (≈ 30.6%) | 32,754 (≈ 22%) | 70,352 (≈ 47.3%) |
| case 3 | TextBlob | mean | 67,035 (≈ 45%) | 16,881 (≈ 11.3%) | 64,796 (≈ 43.6%) |
| case 4 | TextBlob | median | 53,466 (≈ 36%) | 13,748 (≈ 9.2%) | 81,498 (≈ 54.8%) |
Top 25 positive and negative words in title, abstract, and tweets of research articles_
| Title | Abstract | Tweets | |||
|---|---|---|---|---|---|
| Positive | Negative | Positive | Negative | Positive | Negative |
| best | boring | awesome | awful | awesome | awful |
| delicious | devastating | best | bleak | best | bleak |
| excellent | disgusting | delicious | boring | breathtaking | boring |
| greatest | evil | excellent | cruel | delicious | cruel |
| perfect | grim | exquisite | devastating | delightful | devastating |
| superb | vicious | flawless | disgusted | excellent | disgusting |
| wonderful | worst | greatest | dreadful | exquisite | dreadful |
| brilliant | fearful | impressed | evil | greatest | evil |
| ideal | repellent | legendary | grim | impressed | grim |
| incredible | retard | magnificent | gruesome | legendary | gruesome |
| beautiful | base | marvelous | horrible | magnificent | horrible |
| splendid | bloody | masterful | horrific | marvelous | horrific |
| attractive | doubtful | perfect | hysterical | masterful | hysterical |
| experienced | filthy | superb | insane | perfect | insane |
| expressive | grief | wonderful | insulting | priceless | insulting |
| favored | hate | artesian | menacing | superb | miserable |
| great | violent | brilliant | outrageous | wonderful | nasty |
| happy | stupid | ideal | ruthless | brilliant | outrageous |
| intelligent | tragic | incredible | shocking | ideal | pathetic |
| joy | sick | beautiful | terrible | incredible | shocking |
| proud | anger | attractive | terrifying | beautiful | terrible |
| uncommon | crude | brave | vicious | splendid | terrifying |
| unforgettable | frustrated | elect | worst | attractive | vicious |
| win | painful | experienced | fearful | brave | worst |
| remarkable | shocked | expressive | hated | elect | fearful |