Topic categorisation of statements in suicide notes with integrated rules and machine learning.

We describe and evaluate an automated approach used as part of the i2b2 2011 challenge to identify and categorise statements in suicide notes into one of 15 topics, including Love, Guilt, Thankfulness, Hopelessness and Instructions. The approach combines a set of lexico-syntactic rules with a set of models derived by machine learning from a training dataset. The machine learning models rely on named entities, lexical, lexico-semantic and presentation features, as well as the rules that are applicable to a given statement. On a testing set of 300 suicide notes, the approach showed the overall best micro F-measure of up to 53.36%. The best precision achieved was 67.17% when only rules are used, whereas best recall of 50.57% was with integrated rules and machine learning. While some topics (eg, Sorrow, Anger, Blame) prove challenging, the performance for relatively frequent (eg, Love) and well-scoped categories (eg, Thankfulness) was comparatively higher (precision between 68% and 79%), suggesting that automated text mining approaches can be effective in topic categorisation of suicide notes.

Introduction

Automated processing and categorisation of subjective and affective statements (eg, in blogs, tweets, suicide notes) have been both a challenging and hot topic in the humanities and text mining communities in the last decade, in particular with the development of Web 2.0 technologies. Several methods have been developed to automatically identify main messages, sentiments and opinions presented in such environments, across different domains and communities.1–4

The need for quantitative and computational processing of suicide notes in particular has been highlighted as a way to identify any risks of (repeat) attempts as presented in Web 2.0 sources.5,6 The aim of the i2b2 Medical NLP challenge 2011 (Track II) was to classify statements in de-identified suicide notes into 15 different topics (see the challenge description7 for detailed description of the task). These topics included eleven emotions (Hopelessness, Love, Guilt, Thankfulness, Anger, Sorrow, Hopefulness, Happiness Peacefulness, Fear, Pride, Forgiveness) and four other categories such as Instructions, Information, Blame and Abuse. The task was to categorise each line (roughly corresponding to a sentence) into one or more of these topics, or to tag them as referring to none of these topics.

Previous work on computational analysis of suicide notes has been concerned with content analysis (eg, distribution of positive, negative and emotion words).6,8,9 Various discriminative features (eg, emotional concepts, part-of-speech tags (POS), readability scores, etc.) have been considered.6,8 A comparative study between a set of automatic classification algorithms and human counterparts to distinguish genuine suicide notes from simulated ones showed promising results, as nine out of ten machine classification algorithms outperformed the human counterparts (a team of 11 mental health professionals) in distinguishing genuine notes from elicited ones.8

Our approach to the task was a hybrid method that integrates rule-based and machine learning (ML) predictions into a topic-categorisation module. Rule-based predictions combined lexical and syntactic patterns with common expressions empirically associated with a given category. The machine learning module consists of a set of classifiers built using a set of features to provide sentence-level predictions. The two prediction modules were combined using three different approaches, which corresponded to the three runs submitted to the challenge. Our best run combined predictions based on rules and all ML scores, resulting in an (micro) F-measure of 53.36%, with 50.47% recall and 56.61% precision. The following sections describe the method in more detail and provide discussions of the results.

Methodology

An analysis of a set of 300 suicide notes that had been provided by the organisers of the i2b2 2011 challenge7 as the training data revealed that most lines consisted of single sentences, but that there were cases where several sentences appeared in the same line. We further noted that lines that had several topic categories attached to them were likely to have either multi-focal sentences (ie, sentences that contain several statements, either about related or unrelated issues) or indeed several separate sentences. Therefore, the general idea underlying our approach was to determine topic categories for each of the sentences in a note and then integrate sentence-level predictions at the line level.

The system developed for the topic-categorisation consists of four major modules: (A) pre-processing, (B) rule-based predictions, (C) ML-driven predictions and (D) result integration module. Figure 1 provides a detailed system architecture diagram.

Figure 1.

System architecture.

Pre-processing notes. Before sentence splitting, each note was first split into lines, as the final system output was at the line level and a simple way was needed to identify sentences belonging to a particular line. Each line was then filtered to remove a set of symbols (eg, ^, {, *) that caused problems in the further steps in our workflow (eg, in parsing). Given that suicide notes have a number of typos, we performed spelling correction using Google spell checking API10 and then applied the Stanford CoreNLP tools11–13 for tokenisation, sentence splitting, part-of-speech tagging, lemmatisation, shallow parsing and recognition of common named entities (see below). We did not split up multi-focal sentences into separate units.

Rule-based prediction module. We relied on lexico-semantic processing that has been tailored for each of the topic categories by considering (1) category-specific lexical and syntactic patterns (eg, ‘inform <person>’ for Instructions), and (2) common “frozen” expressions empirically associated with a given category (eg, ‘I love you’ for the Love category). All rules (see Table 1 for details) were specified at the literal/lemma level and relied on associated category-specific dictionaries. These dictionaries include common clue terms or expressions (represented as regular expressions to account for variations) associated with particular syntactic patterns and/or expressions indicative of a specific category. These terms were primarily extracted from the training data through manual inspection of a subset of notes, and further extended with synonyms and related words from various online resources, including WordNet14 and Thesaurus.15 For example, strong clues for identification of sentences belonging to the Information class were prepositional phrases (PPs), specifically those expressing relations in space: for instance, a common indicator was a mention of a PP with prepositions in or on followed by a noun phrase whose head described a physical space (eg, box, drawer, bag, pocket, car, trunk, etc). The corresponding semantic sets (eg, for physical space) were defined for each category as a specific dictionary that contained associated nominal or verbal clues. For example, Instructions sentences often contained the word please followed by a verb in its infinitive form optionally followed by a proper noun or pronoun; associated verbs were defined by the Instructions-specific dictionary.

Table 1.

Number of rules and dictionary entries per category.

Topic category	Number of rules	Number of dictionary entries
Instructions	25	61
Hopelessness	21	40
Love	8	0
Guilt	9	14
Information	21	30
Blame	15	0
Thankfulness	6	13
Hopefulness	9	0
Sorrow	6	0
Anger	4	0
Happiness_peacefulness	5	0
Fear	4	0
Pride	4	0
Forgiveness	1	0
Abuse	2	11

Another approach built into rules was to predict topic categories based on specific, typically “ frozen” common laymen expressions. For example, Hopelessness statements were strongly indicated by expressions such as I can’ t take it anymore or I can’ t stand it. The rules were developed using the General Architecture for Text Engineering (GATE)16 and the Java Annotation Pattern Engine (JAPE) grammar formalism. For each rule, the training dataset was used to assess its precision and recall. Precision values were used to rank rules within a given category. Categories associated with the rules that have fired for a given sentence were considered as initial rule-based predictions for that sentence.

ML-based predication module. The main objective of this module was to provide sentence-level predictions using a set of ML models with a pre-defined set of features. Given that the training data set had only five categories that had enough data in order to train ML models (Guilt, Hopelessness, Instructions, Information and Love), we created five ML models for these categories only. The models were designed to recognise sentences from a particular category and to classify all the other sentences in one opposing class (referred as Other). The features used were clustered into four groups: Each of the five classifiers was built in Rapid-Miner26 using the Naive Bayes model with kernel density estimation. Feature selection was performed by a genetic algorithm integrated into RapidMiner and with the Fast Correlation-Based Filter method.27,28 In order to estimate the performance of the selected features, the five-fold cross-validation method was applied as standard.

Lexical features included a set of most significant uni-, bi- and tri-grams extracted for each of the five classes from the training dataset. Significance was measured using the likelihood measure and we have selected the top 500 features for each category. In addition, the features included the lemma of the first finite verb of the sentence, along with the tense assigned to the sentence (determined by a hand-crafted set of rules that relied on part of speech tags and shallow phrases). We also included the presence of negation cues as returned by NegEx.17

Named-entity features were used to indicate the presence of common named entities, such as personal names, addresses, dates, times etc. We used the Stanford CoreNLP suite to identify the following named-entity types: person, location, date, time, money, organization, ideology, nationality, religion, title and misc. As the notes were anonymised by mapping some of these entity type mentions to a fixed set of values (eg, ‘Jane, John, 3333 Burnett Ave.’, etc.), the extraction was also done by simple string matching. Additionally, task-specific semantic classes, for example those referring to a family member, financial information, disease etc. were considered. A careful analysis of sentences from the training corpus (grouped by categories) revealed that sentences from particular groups contained key terms that distinguished them from other categories: for example, sentences from the Hopelessness category often contained disease mentions, whereas Instructions and Information sentences contain financial terms (‘cash’, ‘cheque’, ‘business’ etc.). We also noticed that a number of sentences contained terms indicating sentiment (‘bad’, ‘appalling’, etc.), family members (‘mom’, ‘dad’, ‘sister’ etc.), endearments (‘baby’, honey’ etc.), body parts (‘arm’, ‘chest’, etc.), ache (with many synonyms for pain) and pills (‘pill’, ‘tablet’, ‘capsule’ etc.). Based on our findings, the eight semantic lexicons (corresponding to the mentioned semantic types) were created manually (except for diseases) in a two step process. As a first step we collected all terms by searching through the training data and then in the second step we used the Internet and WordNet to find synonyms for the collected words. We did not perform any statistical tests in order to see if particular semantic classes of words could be useful for separating emotion labels; we rather left it to the ML models and feature selection methods to decide which ones were useful. The disease lexicon was automatically collected from the UMLS18 and the Disease onotology.19

Rule-based features represented a set of binary features provided by the rule-based module (module (B) above). Each rule was represented by a corresponding feature that indicated if that rule has fired in the sentence. We experimented by using the rules precision as feature values, but binary values (fired/not fired) showed better performance on the training data.

Presentation features included attributes that described some aspects of how the statement (in a given sentence) has been made. Previous work8 indicated that such features could be useful for the classification of suicide notes. We considered readability of the sentence, the gender of the person who wrote the note (determined at the note level and then propagated to all sentences) and three features that represented the (local) line number in which the sentence appeared, the number of lines and the number of sentences in the note. In order to calculate the readability, we used the Flesch and Kincaid readability scores20,21 as returned by the Flesh tool.22 In order to extract the gender feature, a set of hand-crafted rules was used to identify greetings (eg, “signed John/Jane”, “love John/Jane” etc.). In cases where there were no greetings found, we used the Koppel-Argamon algorithm23,24 as implemented in Lingua-EN-Gender-1.0.25

Result integration module. The role of this module was to integrate the predictions made by the rule- and ML-based models in order to produce the final output for a given line. Note that both modules provided sentence-level predictions only, which were then combined. Therefore, the system could provide multi-label annotation both at the sentence and line level. We created three different workflows (runs) for the result integration as follows.

In Run 1, the goal was to optimise recall. For a given line, we therefore collected all topic categories returned by all the rules that fired for any of its sentences and all the predictions returned by applying the ML models to them. Based on empirical evidence from the training data, we decided not to use the ML model built for the Information category, given its relatively poor performance (ie, no improvements) compared to other “large” categories (see Table 4). Similarly, we decided not to use the rule-based features in the ML models, again based on the results of experimentation on the training data (the best recall was achieved when the rule-based features were omitted; data not shown).

Table 4.

Per category performance on the training data.

Topic category	Frequency	Run 1			Run 2			Run 3
		P	R	F	P	R	F	P	R	F
Instructions	820	0.6093	0.7476	0.6714	0.8120	0.5634	0.6652	0.7347	0.6280	0.6772
Hopelessness	455	0.6465	0.5626	0.6016	0.8203	0.3912	0.5298	0.6639	0.5253	0.5865
Love	296	0.6916	0.7196	0.7053	0.7946	0.6014	0.6846	0.7208	0.6892	0.7047
Guilt	208	0.5349	0.5529	0.5437	0.6333	0.3654	0.4634	0.5954	0.4952	0.5407
Information	295	0.7521	0.3085	0.4375	0.7561	0.3153	0.4450	0.4256	0.5525	0.4808
Blame	107	0.8108	0.2804	0.4167	0.8108	0.2804	0.4167	0.8108	0.2804	0.4167
Thankfulness	94	0.8871	0.5851	0.7051	0.8750	0.5957	0.7089	0.8871	0.5851	0.7051
Hopefulness	47	0.4595	0.3617	0.4048	0.4595	0.3617	0.4048	0.4595	0.3617	0.4048
Sorrow	51	0.4800	0.2353	0.3158	0.5000	0.2157	0.3014	0.4800	0.2353	0.3158
Anger	69	0.8571	0.0870	0.1579	0.8571	0.0870	0.1579	0.8571	0.0870	0.1579
Happiness_peacefulness	25	0.7273	0.3200	0.4444	0.7273	0.3200	0.4444	0.7273	0.3200	0.4444
Fear	25	0.5000	0.4400	0.4681	0.5000	0.4400	0.4681	0.5000	0.4400	0.4681
Pride	15	0.5000	0.3333	0.4000	0.5000	0.3333	0.4000	0.5000	0.3333	0.4000
Forgiveness	6	1.0000	0.6667	0.8000	1.0000	0.6667	0.8000	1.0000	0.6667	0.8000
Abuse	9	0.5000	0.4444	0.4706	0.5000	0.4444	0.4706	0.5000	0.4444	0.4706
Overall—macro F		0.6066	0.4372	0.4923	0.7031	0.3988	0.4907	0.6003	0.4371	0.4944
Overall—micro F		0.6339	0.5686	0.5995	0.7727	0.44370	0.5637	0.6477	0.5432	0.5909

Note: Frequency represents the number of lines in the training dataset.

Abbreviations: P, precision; R, recall; F, F-measure.

In Run 2, we considered only predictions returned by the rule-based module. The goal for this run was to optimise precision. Final predictions for a given line included all categories returned by the rules from each sentence in that line.

In Run 3 we used all the initial results from the rule-base module (as in Run 2) and a single best prediction from the four ML models (again, the Information category was omitted). In this run, the ML models included the rule-based features (as opposed to Run 1). Overall, we aimed at optimising the F-measure with this run. Predicted labels of the ML models were ranked by prediction confidence as provided by RapidMiner, and the label with the highest confidence was used. These values where comparable since all of the ML models were based on the same approach (Naive Bayes).

Results and Discussion

The task was evaluated on a test dataset containing another set of 300 suicide notes as provided by the organisers. The “gold” annotation topic categories were manually provided for each line by three annotators. The organisers estimated the inter-annotation agreement as 0.546 (using Krippendorff’s alpha coefficient).7

The system performance was primarily estimated using the micro-averaged F-measure, which averages the results across all annotations (line-level). The test results of our system are given in Table 2. Run 1 gave the best results, with the highest F-measure (53.36%) and the highest recall (50.47%). As expected, the best precision (67.17%) was achieved in Run 2, with all predictions coming from the rule-based module. Run 3 was an attempt to compromise between the first two runs, as reflected by the results (but it failed to get the best F-score).

Table 2.

Micro-averaged results on the test data.

	P-micro	R-micro	F-micro
Run 1	0.5661	0.5047	0.5336
Run 2	0.6717	0.3797	0.4851
Run 3	0.5900	0.4764	0.5271

Abbreviations: P, precision; R, recall; F, F-measure.

Category-specific results are given in Table 3. We note that the “large” categories (such as Instructions, Hopelessness, Love and Guilt) have reasonably high and comparable performance, with Love consistently showing the best results (F-measure of 67.34%). The exception is the Information category (F-measure of 29.73%), probably due to the very broad scope of this topic. The results for mid- and low-frequency categories relied on rules only, and typically showed poor performance, with notable exceptions of Thankfulness (F-measure of 72.53%) and Happiness_ peacefulness (F-measure of 53.85%). Still, the rules (Run 2) overall provided relatively high precision (67.17%).

Table 3.

Per-category performance on the test data.

Topic category	Frequency	Run 1			Run 2			Run 3
		P	R	F	P	R	F	P	R	F
Instructions	382	0.5509	0.6649	0.6026	0.7076	0.4372	0.5405	0.5692	0.6571	0.6100
Hopelessness	229	0.5775	0.5371	0.5566	0.7154	0.3843	0.5000	0.5950	0.5197	0.5548
Love	201	0.6802	0.6667	0.6734	0.7943	0.5572	0.6550	0.7832	0.5572	0.6512
Guilt	117	0.4857	0.4359	0.4595	0.6230	0.3248	0.4270	0.5294	0.3846	0.4455
Information	104	0.5000	0.2115	0.2973	0.5000	0.2115	0.2973	0.5000	0.2115	0.2973
Blame	45	0.2381	0.1111	0.1515	0.2381	0.1111	0.1515	0.2381	0.1111	0.1515
Thankfulness	45	0.7174	0.7333	0.7253	0.7174	0.7333	0.7253	0.7174	0.7333	0.7253
Hopefulness	38	0.3077	0.1053	0.1569	0.3077	0.1053	0.1569	0.3077	0.1053	0.1569
Sorrow	34	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
Anger	26	0.5000	0.0385	0.0714	0.5000	0.0385	0.0714	0.5000	0.0385	0.0714
Happiness_peacefulness	16	0.7000	0.4375	0.5385	0.7000	0.4375	0.5385	0.7000	0.4375	0.5385
Fear	13	0.3636	0.3077	0.3333	0.3636	0.3077	0.3333	0.3636	0.3077	0.3333
Pride	9	0.6667	0.2222	0.3333	0.6667	0.2222	0.3333	0.6667	0.2222	0.3333
Forgiveness	8	1.0000	0.1250	0.2222	1.0000	0.1250	0.2222	1.0000	0.1250	0.2222
Abuse	5	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
Overall—macro F		0.4859	0.3064	0.3414	0.5223	0.2664	0.3301	0.4980	0.2940	0.3394

Note: Frequency represents the number of lines in the test dataset.

Abbreviations: P, precision; R, recall; F, F-measure.

Run 3 attempted to optimise F-measure, but the drop in recall was significant probably due to (1) excluding the less confident predictions from the ML models, and (2) using the ML models with rule-based features, which proved to increase precision but have the reverse effect on recall (data not shown). Table 3 also shows the macro-averaged results (averaged over topic categories), which were significantly lower than the micro-averaged ones, given that there were categories (eg, Sorrow and Abuse) with no correct predictions.

When compared to the results on the training dataset (see Table 4), there are drops in the overall micro F-measure of between 6.38 and 7.86 percentage points. There were differences in the performance drops for specific categories: while Love performed mostly consistently (drop of 3%–5%), performance for the Information category dropped between 14 and 19 percentage points, indicating again the wider scope of this category that has not been captured by rules or ML approaches (likely due to lexical variability and limitations of our topic dictionaries). There were also significant drops in performance for Guilt, in particular in the runs that included ML-based predictions, indicating again that the models have not generalised well (see Table 5 for FP and FN examples).

Table 5.

Examples of FPs and FNs for Guilt.

Example sentence	Predicted topic	Correct topic
Mary I’m sorry but it had to be done as I cant go on any longer.	Guilt	Hopelessness
To my beloved children, Please forgive me for taking this step.	Guilt	No annotation
I can not believe I have been so bad a husband as to merit this.	Guilt	Blame
I am no good to My family or myself either.	No annotation	Guilt
I caused you so much unhappiness and worry.	Blame	Guilt
I have sinned and must pay the penalty.	No annotation	Guilt
Love Jane Please forgive me all of you.	Forgiveness	Guilt

While the rules (Run 2) did not fail for some of the “large” categories (Hopelessness, Love and Guilt), there were significant drops for Instructions (a large category) and Information (a wide scope) when compared to the training data. As expected, the rules developed for the mid- and low-frequency categories in principle did not show consistent performance. Notable exceptions are Thankfulness (one of the “easiest” categories to predict) and Happiness_ peacefulness, both of which provided even better performance on the test dataset than on the training data.

We also note that the overall drop in precision for Run 2 (rules only) between the two datasets was significant and even larger than (expected) drop in recall, indicating some confusion between categories (eg, between Instructions and Information; see Table 6). In many cases, the difference between an Instruction and Information is very subtle and requires sophisticated processing (eg, ‘you will find my body’). Information additionally showed a high degree of lexical variability, which was difficult to “capture” with rules or with the ML models. Instructions did show more syntactic constraints, which resulted in reasonable performance overall.

Table 6.

Examples of confusion between Instructions and Information.

Example sentence	Predicted topic	Correct topic
I do not think that it will be necessary to phone him as I have written about the necessary things, Do n’t hesitate to call him if necessary, reversing the charges	Information	Instructions
I think now that they will find my body up Burnet Ave. to the side of the road not to far.	Instructions	Information
This letter gives him authority to turn over to you and Mr. John Johnson, my attotney, free access to my room and personal effects.	Instructions	Information

Another example where the rule-based approach showed a significant drop in precision (from 81% to 24%) was the Blame category (see Table 7 for examples). An inherent limitation of our rule-based approach was reliance on topic-specific dictionaries mainly derived from the dataset. Our manual analysis for Blame did not come up with any specific lexical constraints, which made the rules less productive. In addition, a number of FP cases were due to confusion with Guilt (see tables 5 and 7 for some examples) as with Information and Instructions, the differences can be very subtle.

Table 7.

Example FPs and FNs for the Blame category.

Example sentence	Predicted topic	Correct topic(s)
Perhaps so, I told you two years ago that they were driving me that way	No annotation	Blame
Mom, you should have known what was about to happen after I told you my troubles now I will get my rest	Guilt	Blame
I hope the people who made me do this Go to Hell Jane	Anger	Blame, Anger
I might have killed you if you had been around me in the last 10 months	Blame	Anger
Dear John I ‘m all too sorry I have caused you all the trouble I have	Guilt	Blame
Jane—Forgive me for all the misery I have caused you	Forgiveness	Blame

Tables 3 and 4 show that our approach could profile the Thankfulness and Love categories relatively well, whereas Sorrow and Anger, as well as Abuse proved to be challenging, with virtually no or very few correct predictions in the test dataset. In addition to the training data and examples being scarce for these categories (very few rules and basically no category-specific dictionary, see Table 1), it also seems that wider and deeper affective processing is needed to identify the subtle lexical expression of grief, sadness, disappointment, anger etc. (see Table 8 for some examples). Of course, the task proved to be challenging even for human annotators (Krippendorff’s alpha coefficient of 0.546), with many gold standard annotations that could be considered as questionable or at least inconsistent. This is particularly the case with muti-focal sentences, where many labels seems to be missing (for example, ‘My mind seems to have goen a blank, Forgive me. I love you all. so much.’ is not labelled as Love; ‘(signed) John My wisfe is Mary Jane Johnson 3333 Burnet Ave. Cincinnati, Ohio OH-636-2051 Call her first’ was annotated only as Instructions, but not as Information).

Table 8.

Example of FPs and FNs for the Sorrow and Anger categories.

Example sentence	Predicted topic	Correct topic(s)
Dear Mom, I ‘m sorry for all the Bad things I ‘ve done in the past.	Guilt	Sorrow
I am ill and heart broken	Hopelessness	Sorrow
I grieve that I could not have had the juy of being close to our babies, but that is no one ‘s fault	No annotation	Sorrow
Jane dear I ‘m sorry that I have been making you unhappy—I ‘m all twisted up inside	Sorrow	Guilt, Hopelessness
You left me and did not say anything So darling this is your divorce my darling wife	No annotation	Anger
Hopeing that Father inlow will be very happy for everything now	No annotation	Anger
At one thirty in the morning I was awaking by a Nurse you know how I hate them damn things	Anger	No annotation

In the current approach, we did not try to split individual multi-focal sentences apart and process the parts individually (of course, all sentences in a given line were processed separately). Instead, we hypothesised that we could collect the results from each of the separate ML models and all of the triggered rules at the sentence level, and thus produce multi-label annotations (both at the sentence and consequently at the line level). For example, the sentence ‘Wonderful woman, I love you but can’t take this any longer.’ triggered two rules (one for Love and one for Hopelessness); the ML models for those two classes also gave positive predictions, while the other two ML models predicted the Other label. This resulted in the final prediction for the sentence consisted of both Love and Hopelessness labels. Still, future work may explore if splitting multi-focal sentences would provide better precision, given that some weak evidence in separate parts of the multi-focal sentence could be combined by an ML model to provide (incorrect) higher confidence and thus result in an FP. However, the experiments on both the training and testing data have shown that there was no “over-generation” of labels. The rules were built to have high precision, so in most cases only one rule fired per sentence and cases with more then two fired rules were very rare. An analysis of the ML results revealed that in the majority of cases only one of the four ML models predicted their respective categories for a given sentence. Cases where more than one ML predictions were made seem to be related to multi-focal sentences, and our best results were achieved with all ML predictions taken into account (run 1).

Conclusion

Identification of topics expressed in suicide notes proved to be a challenging task for both manual and automated analyses. Our approach to the prediction of topic categories relied on combining hand-crafted rules (which included both lexical, syntactic and lexico-semantic components) and various features used in the ML models (which included lexical, lexico-semantic and presentation features, and named entities and rules that were linked to corresponding sentences). The results showed reasonable performance for frequent and relatively well-scoped topics (eg, Thankfulness, Love, Instructions), whereas infrequent and non-focused categories (eg, Sorrow, Anger, Blame, Information) proved to be challenging. Future work will need to be informed by a detailed error analysis and in particular further investigations in prediction confusions between various topic categories. The effects of particular features (eg, presentation, named entities, etc.) on performance will also need to be further explored. Still, the current approach not only indicates the limits of the component technologies, but also demonstrates the potentials of combining or selecting different approaches for different topic categories.