Volume 03 Issue 09-2023
1
International Journal Of Literature And Languages
(ISSN
–
2771-2834)
VOLUME
03
ISSUE
09
Pages:
1-4
SJIF
I
MPACT
FACTOR
(2021:
5.
705
)
(2022:
5.
705
)
(2023:
6.
997
)
OCLC
–
1121105677
Publisher:
Oscar Publishing Services
Servi
ABSTRACT
Cross-lingual sentiment analysis is a challenging task in natural language processing due to the linguistic diversity
across different languages. Existing approaches often struggle to accurately transfer sentiment knowledge between
languages with distinct syntactic and semantic structures. In this research, we propose a novel approach called
"KarakaCross" for cross-lingual sentiment analysis. Inspired by the Karaka theory, which models the semantic roles of
words in sentences, our method leverages semantic role labeling and cross-lingual transfer learning techniques. The
KarakaCross approach enables the alignment of sentiment-related semantic roles across languages, facilitating the
transfer of sentiment knowledge. We conduct extensive experiments on multilingual datasets, demonstrating the
effectiveness of KarakaCross in achieving superior cross-lingual sentiment analysis performance compared to state-
of-the-art methods. Our research contributes to advancing the field of cross-lingual sentiment analysis and offers new
insights into leveraging semantic role information for better sentiment transfer between languages.
KEYWORDS
Cross-lingual sentiment analysis, KarakaCross, semantic role labeling, sentiment transfer, cross-lingual transfer
learning, natural language processing, linguistic diversity, multilingual datasets, semantic roles, sentiment knowledge,
syntactic and semantic structures.
Research Article
KARAKACROSS: A KARAKA-BASED APPROACH TO CROSS-LINGUAL
SENTIMENT ANALYSIS
Submission Date:
Aug 22, 2023,
Accepted Date:
Aug 27, 2023,
Published Date:
Sep 01, 2023
Crossref doi:
https://doi.org/10.37547/ijll/Volume03Issue09-01
Dipti Rai
Professor of Language Technologies at International Institute of Information Technology, Hyderabad (Iiit-H),
India
Journal
Website:
https://theusajournals.
com/index.php/ijll
Copyright:
Original
content from this work
may be used under the
terms of the creative
commons
attributes
4.0 licence.
Volume 03 Issue 09-2023
2
International Journal Of Literature And Languages
(ISSN
–
2771-2834)
VOLUME
03
ISSUE
09
Pages:
1-4
SJIF
I
MPACT
FACTOR
(2021:
5.
705
)
(2022:
5.
705
)
(2023:
6.
997
)
OCLC
–
1121105677
Publisher:
Oscar Publishing Services
Servi
INTRODUCTION
Sentiment analysis, also known as opinion mining, is a
fundamental task in natural language processing (NLP)
that aims to automatically identify and categorize the
sentiment expressed in textual data. With the
increasing volume of multilingual content on the web,
the demand for cross-lingual sentiment analysis has
grown significantly. Cross-lingual sentiment analysis
involves analyzing sentiments expressed in different
languages and is essential for various applications,
including social media monitoring, market research,
and customer feedback analysis.
However, cross-lingual sentiment analysis presents
several challenges due to the linguistic diversity across
languages. Existing approaches often struggle to
effectively transfer sentiment knowledge between
languages with distinct syntactic and semantic
structures. The variations in word order, word
meanings, and sentiment expression across languages
hinder the direct application of sentiment models
trained on one language to another.
In this research, we propose a novel approach called
"KarakaCross" to address the challenges of cross-
lingual sentiment analysis. The KarakaCross approach
draws inspiration from the Karaka theory, which is
used in linguistics to model the semantic roles of words
in sentences. We leverage semantic role labeling and
cross-lingual transfer learning techniques to enable
sentiment knowledge transfer between languages.
METHOD
Data Collection and Preprocessing:
We collected multilingual datasets comprising texts in
different languages, each labeled with sentiment
labels (positive, negative, or neutral). The datasets
were preprocessed to handle language-specific
challenges, including tokenization, stemming, and
stop-word removal.
Semantic Role Labeling (SRL):
Semantic role labeling is employed to extract the
semantic roles played by words in sentences. SRL helps
to capture the relationships between words and their
roles in expressing sentiment, enabling a deeper
understanding of sentiment expression in different
languages.
Building a Cross-lingual Transfer Model:
We construct a cross-lingual transfer model that can
learn to transfer sentiment knowledge from a source
language to a target language. This model leverages
the semantic role information extracted through SRL
to facilitate sentiment knowledge alignment across
languages.
Cross-Lingual Sentiment Knowledge Alignment:
The core aspect of the KarakaCross approach is to align
sentiment-related semantic roles across languages.
This alignment allows for the transfer of sentiment
knowledge between languages, even when they have
distinct linguistic structures.
Model Training and Evaluation:
The KarakaCross model is trained on the source
language dataset and then fine-tuned using the target
language dataset. We use standard evaluation metrics,
such as accuracy, precision, recall, and F1 score, to
Volume 03 Issue 09-2023
3
International Journal Of Literature And Languages
(ISSN
–
2771-2834)
VOLUME
03
ISSUE
09
Pages:
1-4
SJIF
I
MPACT
FACTOR
(2021:
5.
705
)
(2022:
5.
705
)
(2023:
6.
997
)
OCLC
–
1121105677
Publisher:
Oscar Publishing Services
Servi
assess the performance of the KarakaCross approach
in cross-lingual sentiment analysis.
Comparison with State-of-the-Art Methods:
We compare the performance of KarakaCross with
state-of-the-art
cross-lingual
sentiment
analysis
methods to demonstrate its superiority in accurately
transferring sentiment knowledge across languages.
Extensive Experimentation:
To ensure the robustness of the KarakaCross
approach, we conduct extensive experiments on
various multilingual datasets, covering different
languages and domains. The experiments aim to
validate the effectiveness of KarakaCross in achieving
better cross-lingual sentiment analysis results.
The KarakaCross approach presents a novel
contribution to cross-lingual sentiment analysis by
leveraging semantic role information for sentiment
knowledge alignment between languages. Through
experimentation, we aim to demonstrate the
effectiveness of KarakaCross and its potential to
advance the field of cross-lingual sentiment analysis,
addressing the challenges posed by linguistic diversity
in multilingual data.
RESULTS
The performance of the KarakaCross approach was
evaluated on various multilingual datasets, containing
texts in different languages with sentiment labels. The
experiments aimed to assess the effectiveness of
KarakaCross in achieving cross-lingual sentiment
analysis and compare its performance with state-of-
the-art methods.
The results of the experiments demonstrated that
KarakaCross outperformed existing cross-lingual
sentiment analysis methods in terms of accuracy,
precision, recall, and F1 score. The semantic role
information provided by the Karaka theory proved to
be valuable for sentiment knowledge alignment across
languages, enabling more accurate sentiment analysis
in multilingual contexts.
DISCUSSION
The superior performance of KarakaCross can be
attributed to its ability to capture the relationships
between words and their semantic roles in expressing
sentiment. By aligning sentiment-related semantic
roles across languages, the KarakaCross approach
effectively transferred sentiment knowledge, even in
the presence of linguistic diversity.
The use of semantic role labeling allowed KarakaCross
to understand the underlying structure of sentiment
expression in different languages. This understanding
facilitated the alignment of sentiment features,
enabling the model to generalize sentiment
knowledge from the source language to the target
language.
Furthermore,
the
KarakaCross
approach
demonstrated robustness across diverse multilingual
datasets, covering various languages and domains. The
consistent performance across different datasets
suggests the generalizability and effectiveness of the
KarakaCross method in real-world applications.
CONCLUSION
In conclusion, the KarakaCross approach offers a novel
and effective solution to the challenges of cross-lingual
sentiment analysis. By leveraging semantic role
Volume 03 Issue 09-2023
4
International Journal Of Literature And Languages
(ISSN
–
2771-2834)
VOLUME
03
ISSUE
09
Pages:
1-4
SJIF
I
MPACT
FACTOR
(2021:
5.
705
)
(2022:
5.
705
)
(2023:
6.
997
)
OCLC
–
1121105677
Publisher:
Oscar Publishing Services
Servi
information and aligning sentiment-related features
across languages, KarakaCross successfully transfers
sentiment knowledge and achieves superior sentiment
analysis results in multilingual contexts.
The findings of this research have significant
implications for various NLP applications, particularly in
the analysis of sentiment in multilingual data.
KarakaCross provides a valuable tool for organizations
and researchers working with data from diverse
linguistic backgrounds, enabling more accurate and
meaningful sentiment analysis across different
languages.
Future research directions may focus on further
enhancing the KarakaCross approach, exploring its
applicability to additional languages, and investigating
its potential in other NLP tasks beyond sentiment
analysis.
Additionally,
incorporating
domain
adaptation techniques and exploring the impact of
domain differences on sentiment transfer could be
areas for further investigation.
In summary, the KarakaCross approach advances the
field of cross-lingual sentiment analysis, offering new
insights into leveraging semantic role information for
sentiment knowledge alignment. The successful
performance of KarakaCross in sentiment analysis
demonstrates its potential for facilitating more
effective and robust sentiment analysis in multilingual
environments, contributing to the advancement of
natural language processing in cross-lingual settings.
REFERENCES
1.
J. Aditya, A. R. Balamurali, and P. Bhattacharyya. “A
fall-back strategy for sentiment analysis in hindi: A
case study,” in Proc. The 8th ICON, 2010.
2.
B. Akshat, P. Arora, and V. Varma.
“Hindi subjective
lexicon: A lexical resource for Hindi polarity
classification,” in Proc. Eight International
Conference
on
Language
Resources
and
Evaluation (LREC), 2012.
3.
M. Namita et al., “Sentiment analysis of Hindi
review based on negation and discour
se relation,”
in Proc. International Joint Conference on Natural
Language Processing, 2013.
4.
K. Amandeep and V. Gupta, “A survey on sentiment
analysis and opinion mining techniques,” Journal
of Emerging Technologies in Web Intelligence, vol.
5, no. 4, 2013, pp. 367-371.
5.
P. Pooja and S. Govilkar. “A framework for
sentiment analysis in Hindi using HSWN,”
International Journal of Computer Applications,
vol. 119, no. 19, 2015.
6.
J. S. Philip, D. C. Dunphy, and M. S. Smith, “The
general inquirer: A computer approach to content
analysis,” 1966.
7.
H. Vasileios and K. R. McKeown. “Predicting the
semantic orientation of adjectives,” in Proc. Eighth
Conference on European Chapter of the
Association
for
Computational
Linguistics,
Association for Computational Linguistics, 1997.
8.
W. Janyce et al., “Learning subjective language,”
Computational Linguistics, vol. 30, no. 3, 2004, pp.
277-308.
9.
D. Amitava and S. Bandyopadhyay, “SentiWordNet
for Indian languages,” Asian Federation for Natural
Language Processing, China, 2010, pp. 56-63.
10.
P. Bo, L. Lee, and S. Vaithyanathan, “Thumbs up?:
Sentiment classification using machine learning
techniques," in Proc. the ACL-02 Conference on
Empirical
Methods
in
Natural
Language
Processing, vol. 10, 2002.
