In the realm of sentiment analysis for restaurant reviews, the advent of Spectral Sentiment Analysis (SSA) techniques has opened new avenues for uncovering nuanced insights. This paper explores the application of SSA methodologies to analyze restaurant reviews, utilizing Spectral Clustering (SC) and Spectral Embedding (SE) techniques. By harnessing the spectral properties of the review data, SSA enables the detection of underlying sentiment patterns, facilitating more accurate sentiment classification. We present a comprehensive overview of SSA methodologies and demonstrate their efficacy through experimental evaluations on real-world restaurant review datasets. Our findings highlight the potential of SSA in enhancing sentiment analysis tasks and provide valuable insights for researchers and practitioners in the field of natural language processing and data analytics.

Spectral Sentiment Analysis, Spectral Clustering, Spectral Embedding

Y. Y, Chen, A. J, Cheng, and W. H. Hsu, “Travel recommendation by mining people attributes and travel group types from community-contributed photos,” IEEE Transactions on Multimedia, vol. 15, no. 6, pp. 1283-1295, Oct. 2015.

P. Kefalas, P. Symeonidis, and Y. Manolopoulos, “A graph- based taxonomy of recommendation algorithms and systems in LBSNs,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no 3, pp.604-622, Mar. 2016.

P. Peng, L. Shou, K. Chen, G. Chen, and S. Wu, “KISS: knowing camera prototype system for recognizing and annotating places-of-interest,”IEEE Transactions on Knowledge and Data Engineering, vol. 28, no 4, pp.994-1006, Apr. 2016.

Personalized Travel Sequence Recommendation on Multi-Source Big Social Media Shuhui Jiang, XuemingQian *, Member, IEEE, Tao Mei, Senior Member, IEEE and Yun Fu, Senior Member, IEEE

X. Wang, Y. L. Zhao, L. Nie, Y. Gao, W. Nie, Z. J. Zha, and T. S. Chua, “Semantic-based location recommendation with multimodal venue semantics,” IEEE Transactions on Multimedia, vol. 17, no. 3, pp. 409-419, Mar. 2015.

S. Jiang, X. Qian, J. Shen, Y. Fu, and T. Mei, “Author topic model based collaborative filtering for personalized poi recommendation,” IEEE Transactions on Multimedia, vol. 17, no. 6, pp. 907–918, 2015.

Q. Hao, R. Cai, X. Wang, J. Yang, Y. Pang, and L. Zhang, “Generating location overviews with images and tags by mining usergenerated travelogues,” in Proceedings of the 17th ACM international conference on Multimedia. ACM, 2009, pp. 801–804.

Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. M. Thalmann, “Time-aware point-of-interest recommendation,” in Proc. SIGIR, 2013, pp. 363-372.

J. D. Zhang and C. Y. Chow, “Spatiotemporal sequential influence modeling for location recommendations: a gravity- based approach,” ACM Transactions on Intelligent Systems and Technology, vol. 7, no. 1, pp. 11, Jan. 2015.

J. D. Zhang and C. Y. Chow, “Point-of-interest recommendations in location-based social networks,” in Proc. SIGSPATIAL, 2016, pp. 26-33.