Comparing Methods for Twitter Sentiment Analysis
Evangelos Psomakelis
1
, Konstantinos Tserpes
1
, Dimosthenis Anagnostopoulos
1
and Theodora Varvarigou
2
1
Dept. of Informatics and Telematics, Harokopio University of Athens, Omirou 9, Tavros, Greece
2
Dept. of Electrical and Computer Engineering,
National Technical University of Athens, Iroon Polytecniou 9, Zografou, Greece
Keywords: Sentiment Analysis, Document Polarity Classification, Lexicon- & Learning-based, Bag of Words, Ngrams,
Ngram Graphs.
Abstract: This work extends the set of works which deal with the popular problem of sentiment analysis in Twitter. It
investigates the most popular document ("tweet") representation methods which feed sentiment evaluation
mechanisms. In particular, we study the bag-of-words, n-grams and n-gram graphs approaches and for each
of them we evaluate the performance of a lexicon-based and 7 learning-based classification algorithms
(namely SVM, Naïve Bayesian Networks, Logistic Regression, Multilayer Perceptrons, Best-First Trees,
Functional Trees and C4.5) as well as their combinations, using a set of 4451 manually annotated tweets.
The results demonstrate the superiority of learning-based methods and in particular of n-gram graphs
approaches for predicting the sentiment of tweets. They also show that the combinatory approach has
impressive effects on n-grams, raising the confidence up to 83.15% on the 5-Grams, using majority vote and
a balanced dataset (equal number of positive, negative and neutral tweets for training). In the n-gram graph
cases the improvement was small to none, reaching 94.52% on the 4-gram graphs, using Orthodromic
distance and a threshold of 0.001.
1 INTRODUCTION
The identification of sentiment in content from
online social networks can be considered the “Holy
Grail” for scientists in the Information Retrieval,
Data Mining and Machine Learning fields. On one
hand there is the challenge of the endeavour itself
which often is a moving target: application
requirements change (topics of interest, media,
available resources, etc) and sentiment analysis
solutions inherently cannot cope automatically with
these changes. On the other hand there is the high
demand, with a large number of organizations and
individuals looking forward to lay their hands on
mechanisms that will automatically harness the
volume of data generated by users and assist them to
evaluate public opinion regarding topics of interest
(products, services, people, concepts, etc).
In this context, Twitter has comprised the most
prominent playground for sentiment analysis
solutions with businesses and scientists alike trying
to tap into its users’ enthusiasm for sharing opinions
publicly online. It is not by chance that numerous
works have suggested methods for implementing
such mechanisms, e.g. (Pak and Paroubek, 2010),
(Go et al., 2009), (Agarwal et al., 2011), (Taboada et
al., 2011).
The most prominent of these methods rely on
two approaches: lexicon-based and learning-based.
In the first, the analysis of a document’s expressed
sentiment is achieved through its breakdown to
words whose sentiment polarity is pre-defined in a
lexicon. In learning-based, supervised classification
algorithms are fed with pre-annotated (in terms of
their sentiment polarity) documents, and are trained
in order to autonomously classify future inputs.
In this work, we investigate some of the most
well-used such methods based on both approaches.
In the set of tests we include possible combinations
of methods and report on their efficiency conducting
experiments using a manually annotated Twitter
dataset.
The major contributions of this work are: the
extended comparison of sentiment polarity
classification methods for Twitter text; the inclusion
of combination of classifiers in the compared set,
225
Psomakelis E., Tserpes K., Anagnostopoulos D. and Varvarigou T..
Comparing Methods for Twitter Sentiment Analysis.
DOI: 10.5220/0005075302250232
In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR-2014), pages 225-232
ISBN: 978-989-758-048-2
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
and; the aggregation and use of a number of
manually annotated tweets for the evaluation of the
methods. Especially regarding the latter, we consider
it to be a main contribution in the sense that from
past experience the automated annotation of tweets
based on the detection of features like the emoticons
("", "", etc) has been problematic since it does
not always reflect the case about the overall
sentiment expressed by the author, especially when
one considers the expression of no-sentiment
("neutral") through the text.
The rest of this report is structured as such:
Section 2, defines the problem of sentiment analysis.
Section 3 provides details about the representation
models that are commonly met in the literature.
Section 4, provides details about the experiments
that were conducted and the results. Finally, Section
5, highlights the main conclusions from this work
and reports on possible future directions for research
and experimentation.
2 PROBLEM FORMULATION
This work adopts and extends the definition of (Pang
and Lee, 2008) for the sentiment polarity problem,
according to which: "...given an opinionated piece of
text, wherein it is assumed that the overall opinion
in it is about one single issue or item, classify the
opinion as falling under one of two opposing
sentiment polarities, or locate its position on the
continuum between these two polarities.". The latter
allows room for defining three classes rather than
the typical two (binary polarity problem). The third
class refers to those text extracts that do not express
either positive or negative sentiment, i.e. they are
neutral.
In this context the problem of document-level
sentiment analysis (Bing, 2011) is addressed. In this
problem it is assumed that documents (in contrast to
sentences or features) are opinionated regarding a
particular topic. In the case of Twitter, the document
is referred to as a "tweet" and it has a very specific
form: a text message containing at most 140
characters.
The purpose is to create a program that will
automatically identify whether the author of a tweet
is expressing positive, negative or no sentiment
about a topic.
The key challenge is to model the text in a way
that the algorithm will use it as input and classify the
text's sentiment polarity. Then, we need to identify
or approximate the function that given the input
modelled document, it will classify the document to
the polarity class it belongs to. Formally and
according to (Aisopos et al., 2012) it is: Given a
collection of documents and the set of all classes
,,
, the goal is to
approximate the unknown target function Φ
: →
, which describes the polarization of documents
according to a golden standard, by means of a
function Φ
: →
that is called the general
polarity classifier.
The identification of the function Φ
involves
the identification of the golden standard, i.e. a
reference model document. The distance of the
tweets in question from the golden standard defines
the class in which they belong. Hence, a third
challenge is the definition of "distance".
An overview of the related work in
representation models and classification algorithms
that influenced this research is presented in the next
Section.
3 RELATED WORK
One of the inherent difficulties when researching
Sentiment Analysis problems is the translation of the
textual data into a format that the computer can
understand and process. For that exact function a
number of Natural Language Processing (NLP)
methods have been developed over the years. In this
paper we will be using three of the most popular
ones, the Bag of Words, the N-Grams and the N-
Gram Graphs.
The Bag of Words (Godbole et al., 2007) may be
classified as the simplest method. According to this
approach, the sentences of the document for which
the machine needs to judge the sentiment it
expresses, are split into a set of words using the
space or the punctuation characters. These words
form a virtual bag of words due to the fact that we
don’t keep any data indicating their ordering or their
connection with their neighbors. Usually this method
is assisted by a dictionary that correlates each word
with a numerical value, showing its sentiment
polarity (Wilson et al., 2005). The lack of contextual
information though makes this correlation inaccurate
in the case of “thwarted expectations” as explained
by both (Pang et al., 2002) and (Turney, 2002). This
case is pretty common in reviews and can confuse
every bag of words algorithm.
The N-Grams are pretty similar to the bag of
words with one big difference; The text is split in
pseudowords of equal length (Pang and Lee, 2008).
The length N is depended by the nature of the input
documents and the problem at hand. Commonly, 2-
KDIR2014-InternationalConferenceonKnowledgeDiscoveryandInformationRetrieval
226
Grams, 3-Grams and 4-Grams are used and other
variations are largely rare (Cavnar and Trenkle,
1994; Fürnkranz, 1998). After the split these
pseudowords form a bag similar to the one formed in
the bag of words method. Using a dictionary in this
case is not useful, because only a small -and in many
cases random- set of pseudowords correspond to real
words (Aisopos et al., 2012). A more customized
way of assigning sentiment values to each N-Gram
is required. More details are provided in Section 4.
The popularity of social media stretched the two
abovementioned methods to their limits. Documents
to be analyzed became short, containing many
abbreviations and neologisms, as well as many
syntax and grammar errors. N-Gram graphs were
suggested as an alternative. The formation of N-
Grams remains at the core of their concept however,
each one of them is depicted as a node in a graph.
The graph denotes the position of each N-Gram in
the sentence and its relation with its neighbors
(Giannakopoulos et al., 2008). As such, each
document is represented by a graph which in turn
contains a number of nodes, each of which
represents an N-Gram, a set of edges representing
the neighboring of two N-Grams and a weight on
each edge, representing the frequency with which
this edge is encountered in the analyzed text. This
frequency shows us how dominant this relation is in
the analyzed text.
A graph can be assigned to a sentiment polarity
class based on its comparison with the golden
standard. In this case, this standard is a merged
graph for each one of the three categories. The
merged graph contains all the N-Grams of the
individual training graphs, all their edges and an
average weight on each edge (Aisopos et al., 2012).
This way each graph can be converted to a
numerical vector, showing the distance from the
three merged graphs. The shortest distance dictates
the class to which the graph belongs.
The next step is to analyze the preprocessed
dataset using various machine learning algorithms
(classifiers) and categorization tools. In this paper
Support Vector Machines (SVMs), Naïve Bayesian
Networks, C4.5, Functional Trees, Best-First Trees,
Multilayer Perceptrons and Logistic Regression
algorithms are examined; in isolation but also in
combinational experiments.
In general, Naïve Bayesian Networks, Functional
Trees, Best-First Trees and the C4.5 algorithms try
to create a categorization tree by dividing the
possible values of each tested variable. Their
difference is in the way they are creating the trees.
For example C4.5, which is based on the ID3
algorithm, is using the Information Gain theory,
which in turn is based on the Entropy theory, in
order to perform the less possible splits, creating the
smallest possible tree (Quinlan, 1996). On the other
hand, Naïve Bayesian Networks use a probabilistic
model in order to maximize their accuracy with no
consideration to the size of the produced tree (John
and Langley, 1995). Best-First Trees decides the
“best” point to split the tree by a more arbitrary
function, specified by the user in order to minimize
the impurity between each new split (Shi, 2007).
Furthermore, Logistic Regression and Support
Vector Machines try to find a mathematical function
that can predict the correlation between the variables
and the class. The Logistic Regression is using
various logistic functions in order to calculate a
function with a graphical representation that has the
minimum distance from each of the training data
points in the multidimensional space (Salazar et al.,
2012). The Support Vector Machines use other
mathematical functions, such as the minimum square
function, in order to create another function with a
similar graphical representation (Mullen and Collier,
2004).
Finally, the Multilayer Perceptrons are an
implementation of artificial neural networks. They
consist of a number of nodes, grouped in different
fully interconnected layers (Haykin, 1994). Each
node is mapping its input values into a set of output
values, using an internal function. This way each
variable will be processed by one or more of node
trees. The outputs of the first layer will become the
inputs of the second and so on until at least one node
of each layer has been activated. That way each one
of the variables can contribute into the final
classification decision with an intelligently
calculated weight.
From a high level perspective, a similar work has
been conducted and reported in (Gonçalves et al.,
2013). The authors tested and compared a number of
complete sentiment analysis methods, i.e. specific
instances of NLP methods and algorithms suggested
in the literature. In the present research the
comparison emphasis was given to the combination
of different NLP methods and machine learning
algorithms detaching from one another. Some of
these combinations were consistent with
standardized sentiment analysis techniques but most
of them were created just for testing purposes.
Similar to the present research, Gonçalves et al. also
tried to combine various methods but instead of
creating a method by combining each algorithm's
decisions in an environment with no a priori
knowledge, they applied weights on the decision of
ComparingMethodsforTwitterSentimentAnalysis
227
each technique, based on a number of statistical
values representing each techniques credibility.
An open-source tool that uses similar techniques
to test sentiment analysis methods is called
RapidMiner (“Technology Blog,” 2012). This tool
employs a large number of natural language
processing methods and classification algorithms in
order to tell us if a text conveys positive, negative or
neutral emotions. RapidMiner’s drawbacks are the
small number of supported languages for some of its
functions, like stemming, and the fact that it does not
support advanced processing methods like the N-
Gram Graphs.
In what follows we provide an analysis of the
various experiments that we conducted in order to
identify the best combination of representation
model, classification algorithm and distance metric.
4 EXPERIMENTS
For the experiments we ran, we used a dataset
consisting of 4451 tweets, assembled by various
datasets that exist on the web, discussing different
topics, from the healthcare system to everyday life
and politics. These tweets were rated manually by a
number of researchers, according their sentiment
polarity towards their subject. That way each tweet
was assigned to a positive, neutral or negative
category, helping us train the machine learning
algorithms we used. After this categorization there
were 1203 positive tweets, 1313 neutral tweets and
1935 negative tweets. This slight tendency towards
negativity affected some of the algorithms, either
causing increased or decreased accuracy.
In order to disassociate the results from the
quality and size of the dataset as much as possible, a
10-fold cross validation method was used to
calculate the accuracy of each experiment, as
discussed in a following paragraph. Therefore even
if a dataset is too small to be considered a valid
ground truth in the classical sentiment analysis sense
it can still be used to provide us with valid results for
the purpose of the current research target; the
comparison between the effectiveness of the tested
methods.
In order to increase the effectiveness of the
categorization on a later phase of the
experimentation, the tweets had to be preprocessed.
On this phase the preprocessing consisted of
removing special characters that added no value to
the sentiment polarity, such as the ‘#’ character.
Then the whole text of every tweet was converted to
lower case characters and every web address in it
was replaced by the keyword URL, since the actual
link was of no importance, the important fact was
that there was a link. As a last step the references to
other users, using the ‘@’ character, were replaced
by the REF keyword since the username of the
referred user had no impact on the sentiment polarity
of the tweet. For example the tweet: “@Elli Expert
settles for biofuel *Says it is efficient, ecofriendly -
... http://t.co/aW14eUJJFH” was converted to “REF
expert settles for biofuel says it is efficient,
ecofriendly -... URL”.
To compare the effectiveness of each experiment
we compared just the confidence ratio of the
categorization. We also filtered out the experiments
that required unrealistic execution times or
computational resources. Each experiment consisted
of a NLP method that translated the textual data in a
format more easily processable by the machine
learning algorithms and one or more machine
learning algorithms that were used to categorize the
textual data. The categories were three: positive,
neutral and negative, depending on the opinion that
the author of the text was expressing about his or her
subject.
Depending on the NLP method used, the
experiments were split into three groups: the Bag of
Words, N-Grams and N-Gram Graphs experiments.
Then, 7 categorization algorithms were applied on
the dataset formed by each NLP method, namely
Support Vector Machine, Naïve Bayesian Networks,
Logistic Regression, Multilayer Perceptrons, Best-
First Trees, Functional Trees and C4.5. In a fourth
experiment family those 7 algorithms were
combined, forming a cooperative decision scheme
with various improvements on their confidence
rates, as it is presented later on.
Starting with the Bag of Words experiments,
each tweet’s text is split into a collection of words.
This split did not take into consideration the relative
position of each word so only the presence or
absence of a word is examined; not its role in the
sentence or its connection with neighboring words.
To calculate each word’s sentiment polarity the
SentiWordNet 3.0 (Baccianella et al., 2010) was
employed. SentiWordNet is a dictionary in which
each word is associated to three numerical scores.
The values of the scores indicate correspondingly
how positive, negative, and “objective” (i.e., neutral)
the word is.
Therefore, in this case, each tweet is modeled by
a triplet of values in [0.0, 1.0]. Each value is the
aggregate of the corresponding positive, negative
and neutral scores of the words contained in the
tweet. To classify the tweet in a single sentiment
KDIR2014-InternationalConferenceonKnowledgeDiscoveryandInformationRetrieval
228
polarity the average of the three values is
considered. By setting a positive and a negative
threshold (gold standard) we were able to categorize
a tweet as positive, neutral or negative. If for
example the positive threshold was 0.5 and the
negative was -0.5 then a tweet with average polarity
-0.3 would be considered neutral, but a tweet with
average polarity 0.6 would be considered positive.
The thresholds in this case were set by trial and
error.
Due to the particularities of tweets, many users
are forced to be creative when it comes to bringing
across their message without using many words. It is
often the case that new words are created which are
shorter than the words with a similar meaning. Thus
the dictionary is proved to be an inaccurate solution
in distinguishing the polarity of each tweet. This is
made obvious when examining the confidence ratios
that resulted by these experiments. In most cases the
results were slightly above the random guess
threshold of 33%. In some cases we noticed a slight
increase up to 35% but it can be considered a
statistical anomaly.
To improve this ratio a number of machine
learning algorithms were introduced in the method.
Those algorithms were used to automatically
generate the positive and negative thresholds,
reducing the number of false categorizations. A 10-
fold cross validation procedure was used in order to
train and test each algorithm in the same dataset.
The resulting ratios were apparently higher but they
still didn’t manage to pass the 50% mark. The best
result that was gathered was at 45.07% successful
categorization using Naïve Bayesian Networks.
The next step was to change the NLP model. In
this case N-Grams were used. The experiments were
conducted for 3-Grams, 4-Grams and 5-Grams. The
N-Grams method splits the text in pseudowords
consisting of N characters each. The higher the N
the more resources required to process the resulting
dataset, up to a limit set by the length of the original
text. After the split, the resulting pseudowords form
a bag of pseudowords similar to the bag of words
that was presented previously. In this case each word
cannot be replaced by a sentiment value because
each tweet is producing its own, unique N-Grams,
and their unique nature prevents any dictionary from
being effective. Thus an extra level of processing is
needed in order to assign a numerical value to each
N-Gram, showing its sentiment polarity.
This process for achieving so includes the
counting of the number of times that each N-Gram
appears in the positive, neutral or negative tweets
and uses that frequency as its value. That way we
have three values for each N-Gram, a positive,
negative and a neutral frequency. We choose to
consider only the greatest of the three frequencies
during the categorization, ignoring the other two, in
order to remain as close as we can to the bag of
words model, which relates each word with only one
numerical value. After that process we can replace
each N-Gram with a numerical value, depending on
its dominant category. If it is a positive N-Gram then
it is replaced by its frequency, if it is a neutral N-
Gram it is replaced by 0 and if it is a negative N-
Gram then it is replaced by its frequency multiplied
by -1. After that the procedure is exactly the same as
the bag of words, i.e. the average of the numerical
values is estimated and thresholds are set by the
classifiers.
Due to the extra processing layer and the
independence from dictionaries and existing words,
one may notice a significant increase in the
confidence rates. An important factor here is the
length of the N-Grams, noticing there was an
improvement in the confidence ratios as the window
N length was raised from 3 to 4 and then 5
characters. The confidence rates in the case of 5-
Grams are almost the double of a random prediction.
The most successful results during these
experiments were:
52.19% on 3-Grams, using logistic regression
and a balanced dataset, limiting the data used
to 3609 tweets, evenly distributed among the
three categories.
65.21% on 4-Grams, using again logistic
regression and the balanced dataset.
75.88% on 5-Grams, using logistic regression
and the balanced dataset.
The third NLP method that was tested was the
N-Gram Graphs. These graphs use the same N-
Grams that were used in the previous experiment
family but now they take into consideration the
interactions that each N-Gram has with its
neighbors. This places the individual N-Grams in a
context, making their polarity value more foolproof
than the rest. The tradeoff for this extra information
is the increased computational cost.
In order to categorize each tweet, we create three
merged graphs, one positive, one neutral and one
negative (golden standards). These merged graphs
contain all the graphs produced by the training data.
Each one contains all the nodes and edges of the
individual ones and averaged weights for each edge.
Each new graph that is tested is compared with each
one of these three graphs, using three different
similarity functions (Aisopos et al., 2012). That way
a vector of nine numerical values, three for each
ComparingMethodsforTwitterSentimentAnalysis
229
merged graph, was produced for each tested tweet.
These vectors were processed by the classifiers in
order to set the appropriate threshold for
categorization.
The resulting merged graphs though were
unrealistically big, making it almost impossible to
process them. To reduce their volume, the graphs
were pruned, by setting an arbitrary pruning
threshold on the edge weights. Experiments were
conducted with two different thresholds in order to
distinguish the effect that the increase or decrease of
the threshold has on the resulting confidence ratio.
According to the results that will be presented
shortly, the pruning threshold greatly affects the
resulting confidence ratio. Our experiments with a
threshold equal to 0.01 reached a limit of 67.12%
using logistic regression. When the threshold was
reduced to 0.001 we got a maximum ratio of 94.53%
using multilayer perceptrons.
Following these results a fourth family of
experiments was implemented, the Combinational
experiments. During these tests all seven algorithms
were employed in a cooperative decision model.
Several ways of making a categorization decision
were studied, taking into consideration the opinion
of every algorithm. Those methods consisted of a
simple majority vote, an average opinion and several
distance functions examining how far the collective
opinion was from the centroid of a positive, neutral
and negative opinion cluster. These distance
functions were the Euclidian distance, the Manhattan
distance, the cosine dissimilarity, the Orthodromic
distance and the Chebychev distance (Hertz, 2006).
The results were encouraging in the N-Gram
experiments, raising the confidence up to 83.15% on
the 5-Grams, using majority vote and the balanced
dataset. In the N-Gram Graph cases the
improvement was small to none, reaching 94.52%
on the 4-Gram Graphs, using Orthodromic distance
and a threshold of 0.001.
Figure 1 depicts the collection of resulting
confidence rates. The center of the circle represents
0% success and the outer circle 100% success. To
compose this graph the results from more than 250
experiments were used. The bottom hemisphere
shows the results of the N-Gram Graph experiments,
both the simple and the combinational ones.
According to these results, the 0.001 threshold
experiments are surpassing very clearly all other
experiments. Instead the 0.01 threshold ones are
about on the same level as the N-Gram experiments
of the upper hemisphere.
5 CONCLUSIONS
In this work we presented an analysis and overview
of the most prominent methods for sentiment
analysis in Twitter. The emphasis was put on the
various NLP models and the combinations of
various classifiers. Lexicon-based methods were
also used.
The results demonstrated the superiority of n-
0,00%
10,00%
20,00%
30,00%
40,00%
50,00%
60,00%
70,00%
80,00%
90,00%
100,00%
SimpleC4,5
SimpleNaïveBayes
SimpleSVM
SimpleLogistic
SimpleMLP
SimpleBFTree
SimpleFT
GraphsC4,5
GraphsNaïveBayes
GraphsSVM
GraphsLogistic
GraphsSimpleLogistic
GraphsMLP
GraphsBftree
GraphsFT
ComboAverage
ComboMajorityComboEuclidean
ComboManhattan
ComboCosine
ComboChebychev
ComboOrthodromicS
ComboOrthodromicC
ComboOrthodromicT
ComboSimpleAverage
ComboSimple
Majority
ComboSimpleEuclidean
ComboSimpleManhattan
ComboSimpleCosine
ComboSimpleChebychev
ComboSimpleOrthodromicS
ComboSimpleOrthodromicC
ComboSimpleOrthodromicT
3‐Gramms0.01Unb
3‐Gramms0.001Unb
4‐Gramms0.01Unb
4‐Gramms0.001Unb
5‐Gramms0.01Unb
5‐Gramms0.001Unb
3‐Gramms0.01Bal
4‐Gramms0.01Bal
5‐Gramms0.01Bal
Figure 1: Summary presentation of the performance of the various methods.
KDIR2014-InternationalConferenceonKnowledgeDiscoveryandInformationRetrieval
230
gram graphs against dictionary techniques in
capturing the expressed sentiment in a document and
specifically in tweets. This outcome may be
explained by the fact that twitter users use a
significant number of abbreviations and internet
slang terms in their posts. These terms are not
included in any formal dictionary and this may be
the reason that the classification process is extremely
difficult using a pre-rated dictionary, even while
using stemming methods. In essence the language
used in Twitter comprises a whole new dialect,
different from common English, and thus a different
dictionary would be appropriate. The results also
demonstrated the improvements that various
combinations of NLP methods and machine learning
algorithms can induce in the confidence rates of
some sentiment analysis techniques.
The innovation of this work is concentrated in
the meticulous evaluation of the efficiency of
various sentiment analysis mechanisms using
manually annotated datasets, as well as in the
demonstration of the possibility to combine
methods, creating new techniques for enhancing the
quality of the outcome.
ACKNOWLEDGEMENTS
This work has been supported by the Consensus
project (http://www.consensus-project.eu) and has
been partly funded by the EU Seventh Framework
Programme, theme ICT-2013.5.4: ICT for
Governance and Policy Modelling under Contract
No. 611688.
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