Such a Wonderful Place: Extracting Sense of Place from Twitter

Siragusa, Giovanni; Leone, Valentina

doi:10.1007/978-3-319-98192-5_55

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Various recent studies using social media, particularly Twitter data, aim to reveal a sense of place. While many computer scientists and computer linguists, on the other hand, discover the power of spatial concepts [95,[97][98][99][100][101][102][103], and likewise, GIScientists discover the potential of semantic analyses [77,94,96,[104][105][106], the dividing line between spatial and non-spatial applications diminishes.…”

Section: Place Names/place Modelingmentioning

confidence: 99%

Revisiting the Role of Place in Geographic Information Science

Merschdorf

Blaschke

2018

IJGI

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Although place-based investigations into human phenomena have been widely conducted in the social sciences over the last decades, this notion has only recently transgressed into Geographic Information Science (GIScience). Such a place-based GIS comprises research from computational place modeling on one end of the spectrum, to purely theoretical discussions on the other end. Central to all research that is concerned with place-based GIS is the notion of placing the individual at the center of the investigation, in order to assess human-environment relationships. This requires the formalization of place, which poses a number of challenges. The first challenge is unambiguously defining place, to subsequently be able to translate it into binary code, which computers and geographic information systems can handle. This formalization poses the next challenge, due to the inherent vagueness and subjectivity of human data. The last challenge is ensuring the transferability of results, requiring large samples of subjective data. In this paper, we re-examine the meaning of place in GIScience from a 2018 perspective, determine what is special about place, and how place is handled both in GIScience and in neighboring disciplines. We, therefore, adopt the view that space is a purely geographic notion, reflecting the dimensions of height, depth, and width in which all things occur and move, while place reflects the subjective human perception of segments of space based on context and experience. Our main research questions are whether place is or should be a significant (sub)topic in GIScience, whether it can be adequately addressed and handled with established GIScience methods, and, if not, which other disciplines must be considered to sufficiently account for place-based analyses. Our aim is to conflate findings from a vast and dynamic field in an attempt to position place-based GIS within the broader framework of GIScience.

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Section: Place Names/place Modelingmentioning

confidence: 99%

Revisiting the Role of Place in Geographic Information Science

Merschdorf

Blaschke

2018

IJGI

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“…Natural Language Processing methods are applied in various studies to analyze user-generated textual contents to extract meaningful information related to places [40][41][42][43][44]. For example, Latent Dirichlet Allocation (LDA) method is used to extract place types [45,46], analyze spatiotemporal interaction of places [47], calculate the similarity of places [12,48], and represent items in location recommender systems [49].…”

Section: Related Workmentioning

confidence: 99%

Extracting Place Functionality From Crowdsourced Textual Data Using Semantic Space Modeling

Karimi,

Mesgari

2023

IEEE Access

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“…Topics can be used to create a location recommender system to suggest places to users. Siragusa and Leone (2018) also extracted sense of place from Twitter data. The data are first filtered and preprocessed, and then the Bag of Words (BoW) is given as input to the LDA model.…”

Section: Related Workmentioning

confidence: 99%

A comparative assessment of machine learning methods in extracting place functionality from textual content

Karimi

Mesgari

Purves

2022

Transactions in GIS

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Places are usually ambiguous and context dependent. Place functionality as a context in place descriptions is one of the prominent and distinguishing features of place. Today, due to the rapid growth of the Internet and social networks, users usually share place-based information. Among the types of information, user-generated textual content is not usually shared in a specific structure. This article aims to extract place functionality using analysis of user-generated textual contents shared by users. For this purpose, we try to extract and predict the place functionality through the whole user reviews as well as only the action verbs expressed in the reviews. In this research, series of comparative experiments was conducted to highlight the influence of action verbs on the prediction of place functionality and to evaluate the predictability across different natural language processing (NLP) methods. We compare the efficiency of three NLP methods in selecting and extracting features, as well as different machine learning text classifier methods to predict place functionality. Evaluation results show that using the whole text of the review can extract place functionality better than using only the action verbs. Also, the bag-of-words feature extraction method performs better than the two embedding methods. Among text classification algorithms, although the multinomial naïve Bayes method is the fastest method, the support vector machine method is more accurate than all other methods, while the execution time of the algorithm is longer. In addition, among

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Such a Wonderful Place: Extracting Sense of Place from Twitter

Cited by 4 publications

References 7 publications

Revisiting the Role of Place in Geographic Information Science

Revisiting the Role of Place in Geographic Information Science

Extracting Place Functionality From Crowdsourced Textual Data Using Semantic Space Modeling

A comparative assessment of machine learning methods in extracting place functionality from textual content

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