Description
Introduction
This assignment will give you experience with a social media corpus (i.e., a collection of posts from Reddit),
Python programming, part-of-speech (PoS) tags, sentiment analysis, and machine learning with scikit-learn.
Your task is to split posts into sentences, tag them with a PoS tagger that we will provide, gather some
feature information from each post, learn models, and use these to classify political persuasion. Sentiment
analysis is an important topic in computational linguistics in which we quantify subjective aspects of
language. These aspects can range from biases in social media for marketing, to a spectrum of cognitive
behaviours for disease diagnosis.
Please check the course bulletin board for announcements and discussion pertaining to this assignment.
Reddit Corpus
We have curated data from Reddit by scraping subreddits, using Pushshift, by perceived political affiliation.
Table 1 shows the subreddits assigned to each of four categories: left-leaning, right-leaning, center/neutral,
and ‘alternative facts’. Although the first three (at least) are often viewed as ordinal segments on a
unidimensional spectrum, here we treat these categories as nominal classes. Here, we use the terms ‘post’
and ‘comment’ interchangeably to mean ‘datum’, i.e., a short segment of user-produced text.
Left (598, 944) Center (599, 872) Right (600, 002) Alt (200, 272)
twoXChromosomes (7, 720, 661) news (2, 782, 9911) theNewRight (19, 466) conspiracy (6, 767, 099)
occupyWallStreet (397, 538) politics (60, 354, 767) whiteRights (118, 008) 911truth (79, 868)
lateStageCapitalism (634, 962) energy (416, 926) Libertarian (3, 886, 156)
progressive (246, 435) canada (7, 225, 005) AskTrumpSupporters (1, 007, 590)
socialism (1, 082, 305) worldnews (38, 851, 904) The Donald (21, 792, 999)
demsocialist (5269) law (464, 236) new right (25, 166)
Liberal (151, 350) Conservative (1, 929, 977)
tea party (1976)
Table 1: Subreddits assigned to each category, with the total posts in each. Since there are over 181M
posts, we sample randomly within each category – the resulting number of available posts for each category
in this assignment is shown on the top row.
These data are stored on the teach.cs servers under /u/cs401/A1/data/. To save space, these files
should only be accessed from that directory (and not copied). All data are in the JSON format.
Copyright © 2022, Frank Rudzicz, Raeid Saqur. All rights reserved.
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Each datum has several fields of interest, including:
ups: the integer number of upvotes.
downs: the integer number of downvotes.
score: [ups − downs]
controversiality: a combination of the popularity of a post and the ratio between ups and downs.
subreddit: the subreddit from which the post was sampled.
author: the author ID.
body: the main textual message of the post, and our primary interest.
id: the unique identifier of the comment.
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Your tasks
1 Pre-processing, tokenizing, and tagging [18 marks]
The comments, as given, are not in a form amenable to feature extraction for classification – there is
too much ‘noise’. Therefore, the first step is to complete a Python program named a1 preproc.py, in
accordance with Section 5, that will read subsets of JSON files, and for each comment perform the following
steps, in order, on the ‘body’ field of each selected comment:
1. Replace all non-space whitespace characters, including newlines, tabs, and carriage returns, with
spaces.
2. Replace HTML character codes (i.e., &…;) with their ASCII equivalent (see http://www.asciitable.com).
3. Remove all URLs (i.e., tokens beginning with http or www).
4. Remove duplicate spaces between tokens.
• Each token must now be separated by a single space.
5. Apply the following steps using spaCy (see below):
• Tagging: Tag each token with its part-of-speech. A tagged token consists of a word, the ‘/’
symbol, and the tag (e.g., dog/NN). See below for information on how to use the tagging module.
The tagger can make mistakes.
• Lemmatization:
– Replace the token itself with the token.lemma . E.g., words/NNS becomes word/NNS. If
the lemma begins with a dash (‘-’) when the token doesn’t (e.g., -PRON- for I, just keep
the token.
– Retain the case of the original token when you perform this replacement. We make two
distinctions here: if the original token is entirely in uppercase, the so is the lemma; otherwise,
keep the lemma in lowercase.
• Sentence segmentation: Add a newline between each sentence. For this assignment, we will use
spaCy’s sentencizer component to segment sentences in a post. Remember to also mark the
end of the post with a newline (watch out for duplicates!).
spaCy: spaCy is a Python library for natural language processing tasks, especially in information
extraction. Here, we only use its ability to obtain part-of-speech tags and lemma, along with sentence
segmentation. For example:
import spacy
nlp = spacy.load(‘en_core_web_sm’, disable=[‘parser’, ‘ner’])
nlp.add_pipe(‘‘sentencizer”)
utt = nlp(u”I know words. I have the best words”)
for sent in utt.sents:
… print(sent.text)
… for token in sent:
… print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
… token.shape_, token.is_alpha, token.is_stop)
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Functionality: The a1 preproc.py program reads a subset of the (static) input JSON files, retains
the fields you care about, including ‘id’, which you’ll soon use as a key to obtain pre-computed features,
and ‘body’, which is text that you preprocess and replace before saving the result to an output file. To
each comment, also add a cat field, with the name of the file from which the comment was retrieved (e.g.,
‘Left’, ‘Alt’,…).
The program takes three arguments: your student ID (mandatory), the output file (mandatory), and
the maximum number of lines to sample from each category file (optional; default=10,000). For example,
if you are student 999123456 and want to create preproc.json, you’d run:
python a1 preproc.py 999123456 -o preproc.json
The output of a1 preproc.py will be used in Task 2.
Your task: Copy the template from /u/cs401/A1/code/a1 preproc.py. There are two functions you
need to modify:
1. In preproc1, fill out each if statement with the associated preprocessing step above.
2. In main, replace the lines marked with TODO with the code they describe. By default, args.a1 dir
points to /u/cs401/A1/, so load the data from args.a1 dir’s data subdirectory.
For this section, you may only use standard Python libraries, except for Step 5. For debugging, you are
advised to either use a different input folder with your own JSON data, or pass strings directly to preproc1.
Subsampling: By default, you should only sample 10,000 lines from each of the Left, Center, Right,
and Alt files, for a total of 40,000 lines. From each file, start sampling lines at index [ID % len(X)], where
ID is your student ID, % is the modulo arithmetic operator, and len(X) is the number of comments in
the given input file (i.e., len(data), once the JSON parse is done). Use circular list indexing if your start
index is too close to the ‘end’.
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2 Feature extraction [22 marks]
The second step is to complete a Python program named a1 extractFeatures.py, in accordance with
Section 5, that takes the preprocessed comments from Task 1, extracts features that are relevant to bias
detection, and builds an npz datafile that will be used to train models and classify comments in Task 3.
For each comment, you need to extract 173 features and write these, along with the category, to a single
NumPy array. These features are listed below. Several of these features involve counting tokens based
on their tags. For example, counting the number of adverbs in a comment involves counting the number
of tokens that have been tagged as RB, RBR, or RBS. Table 4 explicitly defines some of these features
(many of which we have provided as constants in the template); other definitions are available on CDF in
/u/cs401/Wordlists/. You may copy and modify these files, but do not change their filenames.
1. Number of tokens in uppercase (≥ 3 letters long)
2. Number of first-person pronouns
3. Number of second-person pronouns
4. Number of third-person pronouns
5. Number of coordinating conjunctions
6. Number of past-tense verbs
7. Number of future-tense verbs
8. Number of commas
9. Number of multi-character punctuation tokens
10. Number of common nouns
11. Number of proper nouns
12. Number of adverbs
13. Number of wh- words
14. Number of slang acronyms
15. Average length of sentences, in tokens
16. Average length of tokens, excluding punctuation-only tokens, in characters
17. Number of sentences.
18. Average of AoA (100-700) from Bristol, Gilhooly, and Logie norms
19. Average of IMG from Bristol, Gilhooly, and Logie norms
20. Average of FAM from Bristol, Gilhooly, and Logie norms
21. Standard deviation of AoA (100-700) from Bristol, Gilhooly, and Logie norms
22. Standard deviation of IMG from Bristol, Gilhooly, and Logie norms
23. Standard deviation of FAM from Bristol, Gilhooly, and Logie norms
24. Average of V.Mean.Sum from Warringer norms
25. Average of A.Mean.Sum from Warringer norms
26. Average of D.Mean.Sum from Warringer norms
27. Standard deviation of V.Mean.Sum from Warringer norms
28. Standard deviation of A.Mean.Sum from Warringer norms
29. Standard deviation of D.Mean.Sum from Warringer norms
30-173. LIWC/Receptiviti features
Note 1: All of the provided wordlists contain tokens in lowercase. After extracting feature 1 above, you
may convert the tokens in the comment to lowercase as well. Take care to modify only the text and not
the PoS tags , i.e, Dog/NN should become dog/NN and not dog/nn.
Note 2: While calculating features 18-29, only consider those tokens that are present in the text as well
as the wordlists. You may find that some tokens are present in the wordlists with an N/A value; ignore
these as well.
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Functionality: The a1 extractFeatures.py program reads a preprocessed JSON file and extracts
features for each comment therein, producing and saving a D × 174 NumPy array, where the i
th row is the
features for the i
th comment, followed by an integer for the class (0: Left, 1: Center, 2: Right, 3: Alt), as
per the cat JSON field.
The program takes two arguments: the input filename (i.e., the output of a1 preproc), and the output
filename. For example, given input preproc.json and the desired output feats.npz, you’d run:
python a1 extractFeatures.py -i preproc.json -o feats.npz
The output of a1 extractFeatures.py will be used in Task 3.
Your task: Copy the template from /u/cs401/A1/code/a1 extractFeatures.py. There are two
functions you need to modify:
1. In extract1, extract each the first 29 of the aforementioned features from the input string. Features
30-173 should be extracted in extract2.
2. In main, call extract1 and extract2 on each datum and add the results (+ the class) to the feats
array.
When your feature extractor works to your satisfaction, build feats.npz, from all input data.
Norms: Lexical norms are aggregate subjective scores given to words by a large group of individuals.
Each type of norm assigns a numerical value to each word. Here, we use two sets of norms:
Bristol+GilhoolyLogie: These are found in /u/cs401/Wordlists/BristolNorms+GilhoolyLogie.csv,
specifically the fourth, fifth, and sixth columns. These measure the Age-of-acquisition (AoA), imageability (IMG), and familiarity (FAM) of each word, which we can use to measure lexical complexity.
More information can be found, for example, here.
Warringer: These are found in /u/cs401/Wordlists/Ratings Warriner et al.csv, specifically the
third, sixth, and ninth columns. These norms measure the valence (V), arousal (A), and dominance
(D) of each word, according to the VAD model of human affect and emotion. More information on
this particular data set can be found here.
When you compute features 18-29, only consider those words that exist in the respective norms file.
Assume the default value for all of the above features to be zero. Treat the mean and standard
deviation of zero words to be zero.
LIWC/Receptiviti: The Linguistic Inquiry & Word Count (LIWC) tool has been a standard in
a variety of NLP research, especially around authorship and sentiment analysis. This tool provides 85
measures mostly related to word choice; more information can be found here. The company Receptiviti
provides a superset of these features, which also includes 59 measures of personality derived from text.
The company has graciously donated access to its API for the purposes of this course.
To simplify things, we have already extracted these 144 features for you. Simply copy the pre-computed
features from the appropriate uncompressed npy files stored in /u/cs401/A1/feats/. Specifically:
1. Comment IDs are stored in IDs.txt files (e.g., Alt IDs.txt). When processing a comment, find the
index (row) i of the ID in the appropriate ID text file, for the category, and copy the 144 elements
of that row from the associated feats.dat.npy file.
2. The file feats.txt provides the names of these features, in the order provided. For this assignment,
these names will suffice as to their meaning, but you are welcome to obtain your own API license
from Receptiviti in order to get access to their documentation.
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3 Experiments and classification [30 marks]
The third step is to use the features extracted in Task 2 to classify comments using the scikit-learn machine
learning package. Here, you will modify various hyper-parameters and interpret the results analytically. As
everyone has different slices of the data, there are no expectations on overall accuracy, but you are expected
to discuss your findings with scientific rigour. Copy the template from /u/cs401/A1/code/a1 classify.py
and complete the main body and the functions for the following experiments according to the specifications
therein.
The program takes two arguments: the input feature file (the output of a1 extractFeatures), and an
output directory.
python a1 classify.py -i feats.npz -o .
You should create the output directory if it doesn’t already exist. In main, you are expected to load the
data from the input file, partition the input into a train and test set, and call the experiment functions in
order. Use the train test split method to split the data into a random 80% for training and 20% for
testing. For part 3.3, use the entire loaded data set.
To facilitate reproducibility, we have fixed the random seed in a1 classify.py. As a thought experiment, consider all the sources of stochasticity in your assignment.
3.1 Classifiers
Train the following 5 classifiers (see hyperlinks for API) with fit(X train, y train):
1. SGDClassifier: support vector machine with a linear kernel.
2. GaussianNB: a Gaussian naive Bayes classifier.
3. RandomForestClassifier: with a maximum depth of 5, and 10 estimators.
4. MLPClassifier: A feed-forward neural network, with α = 0.05.
5. AdaBoostClassifier: with the default hyper-parameters.
Here, X train is the first 173 columns of your training data, and y train is the last column. Obtain
predicted labels with these classifiers using predict(X test), where X test is the first 173 columns of
your testing data. Obtain the 4 × 4 confusion matrix C using confusion matrix. Given that the element
at row i, column j in C (i.e., ci,j ) is the number of instances belonging to class i that were classified as
class j, compute the following manually, using the associated function templates:
Accuracy : the total number of correctly classified instances over all classifications: A =
P
i P ci,i
i,j ci,j
.
Recall : for each class κ, the fraction of cases that are truly class κ that were classified as κ, R(κ) = Pcκ,κ
j
cκ,j
.
Precision : for each class κ, the fraction of cases classified as κ that truly are κ, P(κ) = Pcκ,κ
i
ci,κ
.
Write the results to the text file a1 3.1.txt in the output directory. You must write to file using the
format strings provided in the template. If you do not follow the format, you may receive a mark
of zero. For each classifier, you will print the accuracy, recall, precision, and confusion matrix. You may
include a written analysis if you are so inclined, but only after the results.
3.2 Amount of training data
Many researchers attribute the success of modern machine learning to the sheer volume of data that is
now available. Modify the amount of data that is used to train your preferred classifier from above in
five increments: 1K, 5K, 10K, 15K, and 20K. These can be sampled arbitrarily from the training set in
Section 3.1. Using only the classification algorithm with the highest accuracy from Section 3.1, report the
accuracies of the classifier to the file a1 3.2.txt using the format string provided in the template. On
one or more lines following the reported accuracies, comment on the changes to accuracy as the number of
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training samples increases, including at least two sentences on a possible explanation. Is there an expected
trend? Do you see such a trend? Hypothesize as to why or why not.
3.3 Feature analysis
Certain features may be more or less useful for classification, and too many can lead to overfitting or other
problems. Here, you will select the best features for classification using SelectKBest according to the
f classif metric as in:
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif
selector = SelectKBest(f_classif, you_figure_it_out)
X_new = selector.fit_transform(X_train, y_train)
pp = selector.pvalues_
In the example above, pp stores the p-value associated with doing a χ
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statistical test on each feature. A
smaller value means the associated feature better separates the classes. Do this:
1. For the 32k training set and each number of features k = {5, 50}, find the best k features according
to this approach. Write the associated p-values to a1 3.3.txt using the format strings provided.
2. Train the best classifier from section 3.1 for each of the 1K training set and the 32K training set,
using only the best k = 5 features. Write the accuracies on the full test set of both classifiers to
a1 3.3.txt using the format strings provided.
3. Extract the indices of the top k = 5 features using the 1K training set and take the intersection with
the k = 5 features using the 32K training set. Write using the format strings provided.
4. Format the top k = 5 feature indices extracted from the 32K training set to file using the format
string provided.
5. Following the above, answer the following questions:
(a) Provide names for the features found in the above intersection of the top k = 5 features. If any,
provide a possible explanation as to why these features may be especially useful.
(b) Are p-values generally higher or lower given more or less data? Why or why not?
(c) Name the top 5 features chosen for the 32K training case. Hypothesize as to why those particular
features might differentiate the classes.
3.4 Cross-validation
Many papers in machine learning stick with a single subset of data for training and another for testing
(occasionally with a third for validation). This may not be the most honest approach. Is the best classifier
from Section 3.1 really the best? For each of the classifiers in Section 3.1, run 5-fold cross-validation given
all the initially available data. Specifically, use KFold. Set the shuffle argument to true.
For each fold, obtain accuracy on the test partition after training on the rest for each classifier. Report
the mean accuracy of each classifier for each of the 5 folds in the order specified in 3.1 to a1 3.4.txt using
the format strings provided. Next, determine whether the accuracy of your best classifier, across the 5
folds, is significantly better than any others. I.e., given vectors a and b, one for each classifier, containing
the accuracy values for each of the respective 5 folds, obtain the p-value from the output S, below:
from scipy import stats
S = stats.ttest_rel(a, b)
print(S.pvalue)
You should have 4 p-values. Report them using the provided format string in the same order as the accuracies, excluding the self-comparison. For example, if the best classifier from 3.1 was the RandomForestClassifier,
then the p-values should be reported in the order: 1 vs. 3, 2 vs. 3, 4 vs. 3, 5 vs. 3.
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4 Bonus [15 marks]
We will give up to 15 bonus marks for innovative work going substantially beyond the minimal requirements.
These marks can make up for marks lost in other sections of the assignment, but your overall mark for
this assignment cannot exceed 100%. The obtainable bonus marks will depend on the complexity of the
undertaking, and are at the discretion of the marker. Importantly, your bonus work should not affect our
ability to mark the main body of the assignment in any way.
You may decide to pursue any number of tasks of your own design related to this assignment, although
you should consult with the instructor or the TA before embarking on such exploration. Certainly, the
rest of the assignment takes higher priority. Some ideas:
• Identify words that the PoS tagger tags incorrectly and add code that fixes those mistakes.
Does this code introduce new errors elsewhere? E.g., if you always tag dog as a noun to correct a
mistake, you will encounter errors when dog should be a verb. How can you mitigate such errors?
• Explore alternative features to those extracted in Task 2. What other kinds of variables would
be useful in distinguishing affect? Consider, for example, the Stanford Deep Learning for Sentiment
Analysis. Test your features empirically as you did in Task 3 and discuss your findings.
• Explore alternative classification methods to those used in Task 3. Explore different hyperparameters. Which hyper-parameters give the best empirical performance, and why?
• Learn about topic modelling as in latent Dirichlet allocation. Are there topics that have an effect
on the accuracy of the system? E.g., is it easier to tell how someone feels about politicians or about
events? People or companies? As there may be class imbalances in the groups, how would you go
about evaluating this? Go about evaluating this.
5 General specifications
As part of grading your assignment, the grader may run your programs and/or python files on test data
and configurations that you have not previously seen. This may be partially done automatically by scripts.
It is therefore important that each of your programs precisely meets all the specifications, including its
name and the names of the files and functions that it uses. A program that cannot be evaluated
because it varies from specifications will receive zero marks on the relevant sections.
The flag –a1 dir can be used to specify an alternate location than /u/cs401/A1 to load data from,
though the submitted files should be generated on the CDF machines. Do not hardwire the absolute
address of your home directory within the program; the grader does not have access to this directory.
All your programs must contain adequate internal documentation to be clear to the graders.
We use Python version 3.9.
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6 Submission requirements
This assignment is submitted electronically. You should submit:
1. All your code for a1 preproc.py, a1 extractFeatures.py, and a1 classify.py (including helper
files, if any).
2. a1 3.1.txt: Report on classifiers.
3. a1 3.2.txt: Report on the amount of training data.
4. a1 3.3.txt: Report on feature analysis.
5. a1 3.4.txt: Report on 5-fold cross-validation.
6. Any lists of words that you modified from the original version.
In another file called ID (use the template on the course web page), provide the following information:
1. your first and last name.
2. your student number.
3. your CDF/teach.cs login id.
4. your preferred contact email address.
5. whether you are an undergraduate or graduate.
6. this statement: By submitting this file, I declare that my electronic submission is my own work, and
is in accordance with the University of Toronto Code of Behaviour on Academic Matters and the
Code of Student Conduct, as well as the collaboration policies of this course.
You do not need to hand in any files other than those specified above. Submit your assignment
on MarkUs. Do not tar or compress your files, and do not place your files in subdirectories. Do not
format your discussion as a PDF or Word document — use plain text only.
7 Working outside the lab
If you want to do some or all of this assignment on your laptop or home computer, for example, you will
have to do the extra work of downloading and installing the requisite software and data. If you take this
route, you take on all associated risks. You are strongly advised to upload regular backups of your work to
CDF/teach.cs, so that if your home machine fails or proves to be inadequate, you can immediately continue
working on the assignment at CDF/teach.cs. When you have completed the assignment, you should try
your programs out on CDF/teach.cs to make sure that they run correctly there. Any component that
does not work on CDF will get zero marks.
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8 Frequently Asked Questions
In this section, we provide a list of clarifications based on common questions from last year’s offering of the
course. In addition to checking this, students needing clarification with respect to the assignment should
check Piazza, where we will have another FAQ section based on questions from this year’s offering of the
course.
• Defining global variables is allowed. This can be done by declaring the variable outside of any defined
functions, or within the ‘main()’ function using the ‘global’ keyword.
• If you’re having trouble loading the English model for spaCy, be sure to first download the model by
using the command ‘python3 -m spacy download en core web sm’ in terminal.
• Always take spaCy’s output to be correct when completing the tokenization and lemmatization. In
the case of lemmas that contain multiple tokens, concatenate the multiple tokens with an underscore
and append the tag to that.
• A multi-character punctuation token is a token consisting of only punctuation characters that is
more than one character long.
• For word-list features, only consider the words that are present in the word-list file.
• When we have 1 data point, the sample std. dev. is 0.
• For Feature 7, “Number of future-tense verbs”, capturing all cases of a future tense verb in text is
not straightforward, and we do not expect a foolproof solution. Identifying the patterns listed in
Table 4 is sufficient.
• To speed up convergence of the MLPClassifier, remember to set the alpha parameter to 0.05.
• Accuracies in the classification portion may differ from student to student based on a number of
benign factors.
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Appendix: Tables
Tag Name Example
CC Coordinating conjunction and
CD Cardinal number three
DT Determiner the
EX Existential there there [is]
FW Foreign word d’oeuvre
IN Preposition or subordinating in, of, like
conjunction
JJ Adjective green, good
JJR Adjective, comparative greener, better
JJS Adjective, superlative greenest, best
LS List item marker (1)
MD Modal could, will
NN Noun, singular or mass table
NNS Noun, plural tables
NNP Proper noun, singular John
NNPS Proper noun, plural Vikings
PDT Predeterminer both [the boys]
POS Possessive ending ’s, ’
PRP Personal pronoun I, he, it
PRP$ Possessive pronoun my, his, its
RB Adverb however, usually, naturally, here, good
RBR Adverb, comparative better
RBS Adverb, superlative best
RP Particle [give] up
SYM Symbol (mathematical or scientific) +
TO to to [go] to [him]
UH Interjection uh-huh
VB Verb, base form take
VBD Verb, past tense took
VBG Verb, gerund or present participle taking
VBN Verb, past participle taken
VBP Verb, non-3rd-person singular present take
VBZ Verb, 3rd-person singular present takes
WDT wh-determiner which
WP wh-pronoun who, what
WP$ Possessive wh-pronoun whose
WRB wh-adverb where, when
Table 2: The Penn part-of-speech tagset—words
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Tag Name Example
# Pound sign £
$ Dollar sign $
. Sentence-final punctuation !, ?, .
, Comma
: Colon, semi-colon, ellipsis
( Left bracket character
) Right bracket character
” Straight double quote
‘ Left open single quote
“ Left open double quote
’ Right close single quote
” Right close double quote
Table 3: The Penn part-of-speech tagset—punctuation
First person:
I, me, my, mine, we, us, our, ours
Second person:
you, your, yours, u, ur, urs
Third person:
he, him, his, she, her, hers, it, its, they, them, their, theirs
Future Tense:
’ll, will, gonna, going+to+VB
Common Nouns:
NN, NNS
Proper Nouns:
NNP, NNPS
Adverbs:
RB, RBR, RBS
wh-words :
WDT, WP, WP$, WRB
Modern slang acronyms:
smh, fwb, lmfao, lmao, lms, tbh, rofl, wtf, bff, wyd, lylc, brb, atm, imao, sml, btw,
bw, imho, fyi, ppl, sob, ttyl, imo, ltr, thx, kk, omg, omfg, ttys, afn, bbs, cya, ez, f2f,
gtr, ic, jk, k, ly, ya, nm, np, plz, ru, so, tc, tmi, ym, ur, u, sol, fml Consider also
https://www.netlingo.com/acronyms.php, if you want, for no-bonus completion.
Table 4: Miscellaneous feature category specifications.
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